Mixed Modeling Action Set

Provides actions for fitting mixed models

mixed Action

Fits linear mixed models.

CASL Syntax
Summary: Input and Output Tables
Parameter Descriptions

CASL Syntax

mixed.mixed <result=results> <status=rc> /

blup={

maxIter=double,

outData={

caslib="string"

compress=TRUE | FALSE

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

solver="DIRECT" | "IOC" | "IOD",

tol=double

byLimit=64-bit-integer,

byOp=integer,

class={{

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string",

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

classglobalopts={

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string"

classLevelsPrint=TRUE | FALSE,

collection={{

details=TRUE | FALSE,

name="string",

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

display={

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

names={"string-1" <, "string-2", ...>},

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod},

estimate={{

adjSimACC=double,

adjSimCV=TRUE | FALSE,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=TRUE | FALSE,

adjSimSeed=64-bit-integer,

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustNONE} | {airMCAdjustSCHEFFE} | {airMCAdjustSIDAK} | {airMCAdjustSIMULATE} | {airMCAdjustT},

alpha=double,

chiSq=TRUE | FALSE,

cl=TRUE | FALSE,

df=double,

divisor={double-1 <, double-2, ...>},

e=TRUE | FALSE,

group={{

levelIndicator={double-1 <, double-2, ...>},

LMatrixValue=double

}, {...}},

joint=TRUE | FALSE,

lower=TRUE | FALSE,

singular=double,

statements={{

adjSimACC=double,

adjSimCV=TRUE | FALSE,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=TRUE | FALSE,

adjSimSeed=64-bit-integer,

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE},

alpha=double,

chiSq=TRUE | FALSE,

cl=TRUE | FALSE,

df=double,

divisor={double-1 <, double-2, ...>},

e=TRUE | FALSE,

effectCoeff={{

coefficients={{coeffEntry-1} <, {coeffEntry-2}, ...>},

terms={effect}

}, {...}},

group={{coeffEntry-1} <, {coeffEntry-2}, ...>},

label="string",

lower=TRUE | FALSE,

singular=double,

subject={{coeffEntry-1} <, {coeffEntry-2}, ...>},

upper=TRUE | FALSE

}, {...}},

subject={{

levelIndicator={double-1 <, double-2, ...>},

LMatrixValue=double

}, {...}},

upper=TRUE | FALSE

}, {...}},

freq="variable-name",

itDetails=TRUE | FALSE,

lsmeans={{

statements={{

adjSimACC=double,

adjSimCV=TRUE | FALSE,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=TRUE | FALSE,

adjSimSeed=64-bit-integer,

adjust="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY" | {airMCAdjustTUKEY} | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSMM} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustDUNNETT} | {airMCAdjustNELSON} | {airMCAdjustT} | {airMCAdjustNONE},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

cl=TRUE | FALSE,

controlLevel={"string-1" <, "string-2", ...>},

corr=TRUE | FALSE,

cov=TRUE | FALSE,

df=double,

e=TRUE | FALSE,

singular=double,

slice={{effect-1} <, {effect-2}, ...>},

terms={{effect-1} <, {effect-2}, ...>} | {"string-1" <, "string-2", ...>}

}, {...}}

}, {...}},

lsmestimate={{

statements={{

adjust={method="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

byLevel=TRUE | FALSE,

chiSq=TRUE | FALSE,

coeff={{namedCoeffDef-1} <, {namedCoeffDef-2}, ...>},

confLimits=TRUE | FALSE,

df=double,

divisor={double-1 <, double-2, ...>},

joint=TRUE | FALSE,

lower=TRUE | FALSE,

obsMargins=TRUE | FALSE,

obsMarginsData={castable},

printCoef=TRUE | FALSE,

printCorr=TRUE | FALSE,

printCov=TRUE | FALSE,

printKCoef=TRUE | FALSE,

singular=double,

terms={{effect-1} <, {effect-2}, ...>},

upper=TRUE | FALSE

}, {...}}

}, {...}},

margins={{

statements={{

adjust={method="BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

byLevel=TRUE | FALSE,

chiSq=TRUE | FALSE,

confLimits=TRUE | FALSE,

controlLevel={"string-1" <, "string-2", ...>},

df=double,

joint=TRUE | FALSE,

lines=TRUE | FALSE,

obsMargins=TRUE | FALSE,

obsMarginsData={castable},

odds=TRUE | FALSE,

oddsRatio=TRUE | FALSE,

printCoef=TRUE | FALSE,

singular=double,

slice={{effect-1} <, {effect-2}, ...>},

sliceControlLevel={"string-1" <, "string-2", ...>},

terms={{effect-1} <, {effect-2}, ...>},

weight=TRUE | FALSE

}, {...}}

}, {...}},

maxClPrint=integer,

method="MIVQUE0" | "ML" | "REML",

mmeq=TRUE | FALSE,

model={

alpha=double,

cl=TRUE | FALSE,

ddf={double-1 <, double-2, ...>},

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

dfMethod="CONTAIN" | "NONE" | "RESIDUAL",

effects={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

noint=TRUE | FALSE,

offset="variable-name",

printDenseSol=TRUE | FALSE,

printSol=TRUE | FALSE,

trial="variable-name",

zeta=double

multimember={{

details=TRUE | FALSE,

name="string",

noEffect=TRUE | FALSE,

stdize=TRUE | FALSE,

vars={"variable-name-1" <, "variable-name-2", ...>},

weight={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

nClassLevelsPrint=integer,

noBlupVar=TRUE | FALSE,

noBound=TRUE | FALSE,

noClPrint=integer,

noInfo=TRUE | FALSE,

noItPrint=TRUE | FALSE,

noPrint=TRUE | FALSE,

noProfile=TRUE | FALSE,

optimization={

absConv=double,

absConvNum=integer,

absFConv=double,

absFConvNum=integer,

absGConv=double,

absGConvNum=integer,

absXConv=double,

absXConvNum=integer,

fConv=double,

fConv2=double,

fConvNum=integer,

fSize=double,

gConv=double,

gConv2=double,

gConvNum=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

optTol=double,

xConv=double,

xConvNum=integer,

xSize=double

output={

allStats=TRUE | FALSE,

alpha=double,

casOut={

caslib="string"

compress=TRUE | FALSE

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>},

lcl="string",

lclPA="string",

noMiss=TRUE | FALSE,

pearson="string",

pearsonPA="string",

pred="string",

predPA="string",

resid="string",

residPA="string",

stderr="string",

stderrPA="string",

student="string",

studentPA="string",

ucl="string",

uclPA="string",

variance="string",

variancePA="string"

outputTables={

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

names={"string-1" <, "string-2", ...>} | {key-1={casouttable-1} <, key-2={casouttable-2}, ...>},

repeated=TRUE | FALSE,

replace=TRUE | FALSE

parms={

hold={integer-1 <, integer-2, ...>},

holdAll=TRUE | FALSE,

initvals={{list-1} <,{list-2}, ...>},

lowerB={double-1 <, double-2, ...>},

noIter=TRUE | FALSE,

parmsData={

caslib="string"

computedOnDemand=TRUE | FALSE

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

computedVarsProgram="string"

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

groupByMode="NOSORT" | "REDISTRIBUTE"

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

name="table-name"

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

singlePass=TRUE | FALSE

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

whereTable={

casLib="string"

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

residVar=double,

upperB={double-1 <, double-2, ...>}

polynomial={{

degree=integer,

details=TRUE | FALSE,

labelStyle={

expand=TRUE | FALSE

exponent="string"

includeName=TRUE | FALSE

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=TRUE | FALSE,

standardize={

method="MOMENTS" | "MRANGE" | "WMOMENTS"

options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

prefix="NONE" | "string"

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

random={{

alpha=double,

cl=TRUE | FALSE,

covType="ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC",

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

effects={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

group={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

noint=TRUE | FALSE,

order=integer,

printSol=TRUE | FALSE,

subject={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

trial="variable-name"

}, {...}},

ranks=TRUE | FALSE,

repeated={{

covType="AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC",

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

effects={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

group={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

noConvert=TRUE | FALSE,

noint=TRUE | FALSE,

order=integer,

printR={integer-1 <, integer-2, ...>},

printRC={integer-1 <, integer-2, ...>},

printRCI={integer-1 <, integer-2, ...>},

printRCorr={integer-1 <, integer-2, ...>},

printRI={integer-1 <, integer-2, ...>},

subject={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

trial="variable-name"

}, {...}},

seed=64-bit-integer,

simpleStat=TRUE | FALSE,

singChol=double,

singRes=double,

singular=double,

slice={{

statements={{

adjust={method="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

confLimits=TRUE | FALSE,

controlLevel={"string-1" <, "string-2", ...>},

df=double,

fTest=TRUE | FALSE,

lines=TRUE | FALSE,

means=TRUE | FALSE,

oddsRatio=TRUE | FALSE,

printCoef=TRUE | FALSE,

printCorr=TRUE | FALSE,

printCov=TRUE | FALSE,

singular=double,

sliceBy={{

levels={"string-1" <, "string-2", ...>},

term={effect}

}, {...}},

term={effect}

}, {...}}

}, {...}},

spline={{

basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

dataBoundary=TRUE | FALSE,

degree=integer,

details=TRUE | FALSE,

knotMax=double,

knotMethod={

equal=integer

list={double-1 <, double-2, ...>}

listWithBoundary={double-1 <, double-2, ...>}

multiscale={

endScale=integer

startScale=integer

}

rangeFractions={double-1 <, double-2, ...>}

knotMin=double,

name="string",

naturalCubic=TRUE | FALSE,

separate=TRUE | FALSE,

split=TRUE | FALSE,

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

store={

caslib="string",

label="string",

lifetime=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=TRUE | FALSE,

replace=TRUE | FALSE,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

table={

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

test={{

statements={{

chiSq=TRUE | FALSE,

df=double,

eType={integer-1 <, integer-2, ...>},

hType={integer-1 <, integer-2, ...>},

terms={{effect-1} <, {effect-2}, ...>}

}, {...}}

}, {...}},

timing=TRUE | FALSE,

weight="variable-name"

;

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
lsmestimate	required parameterstatements (and nested parameter obsMarginsData)	specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.
margins	required parameterstatements (and nested parameter obsMarginsData)	specifies the effects and related parameters for predictive margins of fixed effects.
parms	parmsData	specifies the initial covariance values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
blup	required parameteroutData	creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	Stores linear mixed models to a blob (binary large object).

Parameter Descriptions

blup={mixedBlupStmt}

creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.

The mixedBlupStmt value can be one or more of the following:

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

* outData={casouttable}

names the table on the server that contains BLUE and BLUP values.

For more information about specifying the outData parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

solver="DIRECT" | "IOC" | "IOD"

names the solver that is used to obtain the BLUE and BLUP.

Default	IOC

DIRECT

requires storing mixed model equations (MMEq) in memory and computing the Cholesky decomposition of MMEq.

IOC

requires storing mixed model equations (MMEq) in memory and iterates on MMEq to solve for the solutions.

IOD

does not build mixed model equations; instead it iterates on data to solve for the solutions.

tol=double

specifies the convergence criteria for solving the BLUP by the iteration method.

Alias	tolerance
Minimum value	0

byLimit=64-bit-integer

specifies that the analysis will not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

byOp=integer

specifies the method to use for BY-group processing.

Default	0
Range	0–1

class={{classStatement-1} <, {classStatement-2}, ...>}

names the classification variables to be used as explanatory variables in the analysis.

Aliases	classVars
Aliases	nominal

The classStatement value can be one or more of the following:

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level for this variable.

Default	FALSE

descending=TRUE | FALSE

when set to True, reverses the sort order that is imposed by the order parameter.

Default	FALSE

ignoreMissing=TRUE | FALSE

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	FALSE

levelizeRaw=TRUE | FALSE

when set to True, bases levelization for this variable on raw values.

Default	FALSE

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

specifies the parameterization method for the classification variable or variables. The default is GLM when none of the variables specified in the vars parameter includes a param parameter; otherwise, the default is REFERENCE.

ref="FIRST" | "LAST" | double | "string"

specifies the reference level to use when you specify a nonsingular parameterization in the param parameter. For an individual variable, you can specify the level of the variable to use as the reference level. If the action supports the global class options parameter, then you can specify FIRST or LAST.

* vars={"variable-name-1" <, "variable-name-2", ...>}

specifies the classification variables.

Alias	name

classglobalopts={classopts}

lists options that apply to all classification variables.

Long form	classglobalopts={param="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"}
Shortcut form	classglobalopts="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

The classopts value can be one or more of the following:

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level for this variable.

Default	FALSE

descending=TRUE | FALSE

when set to True, reverses the sort order that is imposed by the order parameter.

Default	FALSE

ignoreMissing=TRUE | FALSE

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	FALSE

levelizeRaw=TRUE | FALSE

when set to True, bases levelization for this variable on raw values.

Default	FALSE

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

For more information, see the description of the order subparameter in the class parameter (Shared Concepts).

param="EFFECT" | "GLM" | "ORDINAL" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

For more information, see the description of the param subparameter in the class parameter (Shared Concepts).

ref="FIRST" | "LAST" | double | "string"

classLevelsPrint=TRUE | FALSE

when set to False, suppresses the display of class levels.

Default	TRUE

collection={{collection-1} <, {collection-2}, ...>}

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

The collection value can be one or more of the following:

details=TRUE | FALSE

when set to True, requests a table that shows additional details that are related to this effect.

Default	FALSE

* name="string"

specifies the name of the effect.

* vars={"variable-name-1" <, "variable-name-2", ...>}

specifies a set of variables that are treated as a single effect that has multiple degrees of freedom. The columns in the design matrix that are contributed by a collection effect are the design columns of its constituent variables in the order in which they appear in the definition of the collection effect.

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod}

specifies the design matrix method.

The mixedDmMethod value can be one or more of the following:

* method="DENSE" | "NOSPEC" | "SPARSE"

smmethod="BASIC" | "NDORDERING"

estimate={{estimateStmt-1} <, {estimateStmt-2}, ...>}

specifies the effects, their coefficients, and the options for a customized linear estimation.

The estimateStmt value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

adjSimCV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

adjSimReport=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

cl=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor={double-1 <, double-2, ...>}

specifies a list of values to divide the coefficients.

e=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Default	FALSE

group={{coeffEntry-1} <, {coeffEntry-2}, ...>}

defines an effect that specifies heterogeneity in the covariance structure of G for mixed models. All observations that have the same level of the group effect have the same covariance parameters. Each new level of the group effect produces a new set of covariance parameters that has the same structure as the original group.

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

joint=TRUE | FALSE

requests a joint test for the LS-Means.

Default	FALSE

lower=TRUE | FALSE

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

* statements={{estimateList-1} <, {estimateList-2}, ...>}

The estimateList value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

adjSimCV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

adjSimReport=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

cl=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor={double-1 <, double-2, ...>}

specifies a list of values to divide the coefficients.

e=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Default	FALSE

* effectCoeff={{coeffDefinition-1} <, {coeffDefinition-2}, ...>}

specifies an effect and its non-positional coefficients.

The coeffDefinition value can be one or more of the following:

coefficients={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

* terms={effect}

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

specifies the variables to be nested within the term that is defined by the vars parameter. For terms with a BAR or CROSS interaction, the nest corresponds to the last variable in the vars parameter. For terms with no interaction, the nest is distributed across all variables that are listed in the vars parameter.

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

* label="string"

specifies a name for every row of the multirow estimate.

Alias	name

lower=TRUE | FALSE

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

subject={{coeffEntry-1} <, {coeffEntry-2}, ...>}

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

upper=TRUE | FALSE

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	FALSE

subject={{coeffEntry-1} <, {coeffEntry-2}, ...>}

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

upper=TRUE | FALSE

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	FALSE

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

itDetails=TRUE | FALSE

when set to True, adds the covariance values to the iteration history at each step of the optimization.

Default	FALSE

lsmeans={{lsmeansStatement-1} <, {lsmeansStatement-2}, ...>}

specifies the effects and related parameters for least squares means of fixed effects.

* statements={{lsmeansList-1} <, {lsmeansList-2}, ...>}

The lsmeansList value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

adjSimCV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01
Range	0–1

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604
Minimum value	0

adjSimReport=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY" | {airMCAdjustTUKEY} | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSMM} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustDUNNETT} | {airMCAdjustNELSON} | {airMCAdjustT} | {airMCAdjustNONE}

determines the adjustment method for multiple comparisons of LS-Means differences.

The airMCAdjustTUKEY value is specified as follows:

* method="TUKEY"

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSMM value is specified as follows:

* method="GT2" | "SMM"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustDUNNETT value is specified as follows:

* method="DUNNETT"

The airMCAdjustNELSON value is specified as follows:

* method="NELSON"

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

cl=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	FALSE

controlLevel={"string-1" <, "string-2", ...>}

displays the differences with a control level of the specified least squares means effects.

corr=TRUE | FALSE

when set to True, displays the estimated correlation matrix of the least squares means.

Default	FALSE

cov=TRUE | FALSE

when set to True, displays the estimated covariance matrix of the least squares means.

Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

e=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

slice={{effect-1} <, {effect-2}, ...>}

specifies effects by which to partition interaction LSMEANS effects.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* terms={{effect-1} <, {effect-2}, ...>} | {"string-1" <, "string-2", ...>}

specifies effects in the model for the estimates of the least squares means.

The effect value is specified as follows:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

lsmestimate={{lsmestimateStatement-1} <, {lsmestimateStatement-2}, ...>}

specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.

* statements={{lsmestimateList-1} <, {lsmestimateList-2}, ...>}

The lsmestimateList value can be one or more of the following:

adjust={method="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

byLevel=TRUE | FALSE

computes separate margins.

Default	FALSE

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

coeff={{namedCoeffDef-1} <, {namedCoeffDef-2}, ...>}

The namedCoeffDef value can be one or more of the following:

coefficients={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

divisor=double

specifies a list of values to divide the coefficients.

* name="string"

specifies a name for every row of the multirow estimate.

confLimits=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor={double-1 <, double-2, ...>}

specifies a list of values to divide the coefficients.

joint=TRUE | FALSE

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Default	FALSE

lower=TRUE | FALSE

performs one-sided, lower-tailed inference.

Alias	lowerTailed
Default	FALSE

obsMargins=TRUE | FALSE

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	FALSE

obsMarginsData={castable}

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=TRUE | FALSE

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	FALSE

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the names of the computed variables to create. Specify an expression for each variable in the computedVarsProgram parameter. If you do not specify this parameter, then all variables from computedVarsProgram are automatically included.

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

orderBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use for ordering observations within partitions. This parameter applies to partitioned tables, or it can be combined with variables that are specified in the groupBy parameter when the value of the groupByMode parameter is set to REDISTRIBUTE.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

singlePass=TRUE | FALSE

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	FALSE

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

specifies an input table that contains rows to use as a WHERE filter. If the vars parameter is not specified, then all the variable names that are common to the input table and the filtering table are used to find matching rows. If the where parameter for the input table and this parameter are specified, then this filtering table is applied first.

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

printCoef=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	FALSE

printCorr=TRUE | FALSE

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	FALSE

printCov=TRUE | FALSE

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	FALSE

printKCoef=TRUE | FALSE

when set to True, displays the K matrix coefficients for the specified effects.

Alias	elsm
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

* terms={{effect-1} <, {effect-2}, ...>}

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

upper=TRUE | FALSE

performs one-sided, upper-tailed inference.

Alias	upperTailed
Default	FALSE

margins={{marginsStatement-1} <, {marginsStatement-2}, ...>}

specifies the effects and related parameters for predictive margins of fixed effects.

* statements={{marginsList-1} <, {marginsList-2}, ...>}

The marginsList value can be one or more of the following:

adjust={method="BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

byLevel=TRUE | FALSE

computes separate margins.

Default	FALSE

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

confLimits=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	FALSE

controlLevel={"string-1" <, "string-2", ...>}

displays the differences with a control level of the specified least squares means effects.

df=double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

joint=TRUE | FALSE

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Alias	fTest
Default	FALSE

lines=TRUE | FALSE

produces 'Lines' display for pairwise LS-Means difference.

Default	FALSE

obsMargins=TRUE | FALSE

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	FALSE

obsMarginsData={castable}

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=TRUE | FALSE

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	FALSE

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

orderBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

singlePass=TRUE | FALSE

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	FALSE

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

odds=TRUE | FALSE

reports odds of levels of fixed effects if permissible by the link function.

Default	FALSE

oddsRatio=TRUE | FALSE

reports differences of LS-Means in terms of odds ratios by the link function.

Default	FALSE

printCoef=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

slice={{effect-1} <, {effect-2}, ...>}

specifies effects by which to partition interaction LSMEANS effects.

Alias	sliceBy

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

sliceControlLevel={"string-1" <, "string-2", ...>}

requests slice effects differences with a control level of each of the specified LSMEANS effects.

sliceDiff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

determines the type of simple effects differences.

Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

* terms={{effect-1} <, {effect-2}, ...>}

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

weight=TRUE | FALSE

when set to True, computes the weighted predictive margins.

Default	FALSE

maxClPrint=integer

specifies the maximum levels of classification variables to print in the ClassLevels table.

Default	20

method="MIVQUE0" | "ML" | "REML"

specifies the estimation method for covariance estimation analysis.

Default	REML

MIVQUE0

performs minimum variance quadratic unbiased estimation (MIVQUE0).

ML

performs maximum likelihood estimation (ML).

REML

performs residual (restricted) maximum likelihood estimation (REML).

mmeq=TRUE | FALSE

when set to True, displays the mixed model equations table.

Default	FALSE

* model={mixedModelStmt}

names the dependent variable, explanatory effects, and model options.

The mixedModelStmt value can be one or more of the following:

alpha=double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

cl=TRUE | FALSE

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	FALSE

ddf={double-1 <, double-2, ...>}

specifies a list of the customized denominator degrees of freedom for the fixed effects.

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=TRUE | FALSE

when set to True, reverses the sort order of the response categories. When the descending parameter is set to True and a value is specified for the order parameter, the action orders the response categories according to the requested order and then reverses that order.

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

specifies the type of the response variable. The types NOMINAL, ORDINAL and BINARY specify that the response variable should be levelized. When the value of this parameter is INTERVAL, all other parameters specified for this response variable are ignored and the response variable is not levelized.

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

specifies the reference level that is used for the response variable. Valid parameter values are a quoted string that specifies a valid level for the response variable or FIRST or LAST. FIRST and LAST refer to the first and last ordered value of the variable, respectively.

dfMethod="CONTAIN" | "NONE" | "RESIDUAL"

specifies the degrees of freedom method.

Alias	ddfm
Default	RESIDUAL

dist="BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL"

specifies the response distribution for the model.

effects={{effect-1} <, {effect-2}, ...>}

specifies a list of effects that define the model. Each term in this list is made up of variables specified in the vars parameter and their interaction (which can be NONE, CROSS, or BAR). When the interaction is BAR, it can be limited by the maxInteract parameter.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

link="CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO"

specifies the link function for the model.

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

offset="variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

printDenseSol=TRUE | FALSE

when set to True, displays the fixed effects estimates.

Default	FALSE

printSol=TRUE | FALSE

when set to True, displays the fixed effects estimates.

Default	FALSE

trial="variable-name"

specifies a positive numeric variable that is the number of trials. When you specify a trial variable, the response variable is called the events variable and it must contain the number of positive responses (or events).

zeta=double

specifies a value to tune the estimability check.

Range	0–1

multimember={{multimember-1} <, {multimember-2}, ...>}

uses one or more classification variables specified in the vars parameter in such a way that each observation can be associated with one or more levels of the union of the levels of the classification variables.

For more information about specifying the multimember parameter, see the common multimember parameter (Appendix A: Common Parameters).

nClassLevelsPrint=integer

limits the display of class levels. The value 0 suppresses all levels.

Minimum value	0

noBlupVar=TRUE | FALSE

when set to True, suppresses computing the covariances of random-effects estimates that are used in output statistics.

Default	FALSE

noBound=TRUE | FALSE

when set to True, enforces no boundary restriction for estimating covariance parameters.

Default	FALSE

noClPrint=integer

suppresses the display of the Class Level Information table if you do not specify a number. If you specify a number, the values of the classification variables are displayed only for variables whose number of levels is less than number.

Default	0

noInfo=TRUE | FALSE

when set to True, suppresses the display of the Model Information, Number of Observations, and Dimensions tables.

Default	FALSE

noItPrint=TRUE | FALSE

when set to True, suppresses the display of the Iteration History table.

Default	FALSE

noPrint=TRUE | FALSE

when set to True, suppresses the display of results.

Default	FALSE

noProfile=TRUE | FALSE

when set to True, includes the residual variance as one of the covariance values in the optimization iterations.

Default	FALSE

optimization={mixedOptimizationStmt}

specifies the technique and options for performing the optimization.

Long form	optimization={technique="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"}
Shortcut form	optimization="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"

The mixedOptimizationStmt value can be one or more of the following:

absConv=double

specifies the absolute function convergence criterion.

Minimum value	0

absConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absFConv=double

specifies the absolute function difference convergence criterion.

Minimum value	0

absFConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Minimum value	0

absGConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absXConv=double

specifies the absolute convergence criterion.

Minimum value	0

absXConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Minimum value	0

fConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

fSize=double

specifies the value to use for both the relative function convergence criterion and the relative gradient convergence criterion.

Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Minimum value	0

gConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Default	200
Minimum value	0

maxTime=double

specifies the maximum allowed computing time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

optTol=double

defines the measure by which you can decide whether the value in the current iteration is an acceptable approximation of a local minimum.

Default	1E-05
Minimum value	0

technique="ACTIVESET" | "CONGRA" | "DBLDOG" | "IPDIRECT" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "QUANEW" | "TRUREG"

specifies the optimization technique.

ACTIVESET

uses the active set method.

CONGRA

uses the conjugate gradient method, which requires first-order derivatives.

DBLDOG

uses the double-dogleg method, which requires first-order derivatives.

IPDIRECT

uses the direct interior point method.

LBFGS

uses the Limited-memory BFGS solver, which requires first-order derivatives.

NEWRAP

uses the Newton-Raphson method with line search and ridging, which requires first- and second-order derivatives.

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed using the initial covariance values.

NRRIDG

uses the Newton-Raphson method with ridging, which requires first- and second-order derivatives.

QUANEW

uses the dual quasi-Newton method, which requires first-order derivatives.

TRUREG

uses the trust region method, which requires first- and second-order derivatives.

xConv=double

specifies the relative convergence criterion.

Minimum value	0

xConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

xSize=double

specifies the value to use for the relative convergence criterion.

Minimum value	0

output={mixedOutputStmt}

creates a table on the server that contains observationwise statistics, which are computed after fitting the model.

The mixedOutputStmt value can be one or more of the following:

allStats=TRUE | FALSE

when set to True, requests all available statistics.

Default	FALSE

alpha=double

specifies the significance level to use in output statistics. The default value is 0.05.

Default	0.05
Range	0–1

* casOut={casouttable}

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>}

specifies a list of one or more variables to be copied from the input table to the output table. You can alternatively specify the value ALL, ALL_MODEL, or ALL_NUMERIC, which respectively copies all variables, all variables used in the modeling, or all numeric variables from the input table to the output table.

lcl="string"

names the lower bound of a confidence interval for the linear predictor.

lclPA="string"

names the lower bound of a confidence interval for the marginal linear predictor.

noMiss=TRUE | FALSE

when set to True, writes only observations that are used in the analysis to the output data table. By default, output statistics for all observations are written to the output data table.

Default	FALSE

pearson="string"

names the Pearson-type residual.

pearsonPA="string"

names the marginal Pearson-type residual.

pred="string"

names the linear predictor. If no output statistics are specified, then this is the default.

Aliases	p
Aliases	predicted

predPA="string"

names the marginal linear predictor.

resid="string"

names the residual, which is calculated as ACTUAL minus PREDICTED.

Aliases	r
Aliases	residual

residPA="string"

names the marginal standard deviation of the linear predictor.

stderr="string"

names the standard deviation of the linear predictor.

stderrPA="string"

names the marginal standard deviation of the linear predictor.

student="string"

names the studentized residuals, which are the residuals divided by their standard errors.

studentPA="string"

names the marginal residual.

ucl="string"

names the upper bound of a confidence interval for the linear predictor.

uclPA="string"

names the upper bound of a confidence interval for the marginal linear predictor.

variance="string"

names the conditional variance of the response variable.

variancePA="string"

names the marginal variance of the response variable.

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	displayOut

parms={mixedParmsStmt}

specifies the initial covariance values.

The mixedParmsStmt value can be one or more of the following:

hold={integer-1 <, integer-2, ...>}

holds all or partial covariance values.

Alias	eqcons

holdAll=TRUE | FALSE

when set to True, holds all covariance values.

Default	FALSE

initvals={{list-1} <,{list-2}, ...>}

specifies the initial covariance values.

lowerB={double-1 <, double-2, ...>}

specifies the lower boundary for covariance values.

noIter=TRUE | FALSE

when set to True, performs no iteration for estimating covariance parameters.

Default	FALSE

parmsData={castable}

names the data table that contains the initial covariance values.

For more information about specifying the parmsData parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

Alias	pData

residVar=double

specifies a value for residual variance and excludes it from optimization search.

Minimum value	1E-08

upperB={double-1 <, double-2, ...>}

specifies the upper boundary for covariance values.

polynomial={{polynomial-1} <, {polynomial-2}, ...>}

specifies a polynomial effect. All specified variables must be numeric. A design matrix column is generated for each term of the specified polynomial. By default, each of these terms is treated as a separate effect for the purpose of model building.

For more information about specifying the polynomial parameter, see the common polynomial parameter (Appendix A: Common Parameters).

Alias	poly

random={{mixedRandomStmt-1} <, {mixedRandomStmt-2}, ...>}

specifies random effects and related options. Notice that no-intercept is inherited from the model specification where default value is FALSE, but the default value in random specification is TRUE.

The mixedRandomStmt value can be one or more of the following:

alpha=double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

cl=TRUE | FALSE

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	FALSE

covType="ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC"

specifies the type of covariance structure.

Default	VC

ANTE

specifies the first-order antedependence covariance structure.

AR

specifies the first-order autoregressive covariance structure.

ARH

specifies the heterogeneous first-order autoregressive covariance structure.

ARMA

specifies the autoregressive moving average covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

FA

specifies the factor-analytic covariance structure.

FA0

specifies the no-diagonal factor-analytic covariance structure.

FA1

specifies the equal-diagonal factor-analytic covariance structure.

HF

specifies the Huynh-Feldt covariance structure.

TOEP

specifies the Toeplitz covariance structure.

TOEPH

specifies the heterogeneous Toeplitz covariance structure.

UC

specifies the uniform correlation covariance structure.

UN

specifies the unstructured covariance.

UNR

specifies the unstructured correlation covariance structure.

VC

specifies the variance components structure.

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=TRUE | FALSE

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

effects={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

order=integer

specifies the order of covariance structure.

printSol=TRUE | FALSE

when set to True, displays the random-effects estimates. You can set this parameter to True by specifying the alpha or cl parameters in the model specification.

Default	FALSE

subject={{effect-1} <, {effect-2}, ...>}

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

trial="variable-name"

ranks=TRUE | FALSE

when set to True, displays the table of ranks of the matrices X, (XZ), and MMEq.

Default	FALSE

repeated={{mixedRepeatedStmt-1} <, {mixedRepeatedStmt-2}, ...>}

specifies repeated effects and related options. Notice that no-intercep is inherited from the model specification where default value is FALSE, but the default value in the repeated measures analysis is TRUE.

The mixedRepeatedStmt value can be one or more of the following:

covType="AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC"

specifies the type of covariance structure.

Default	VC

AR

specifies the first-order autoregressive covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

UN

specifies the unstructured covariance.

VC

specifies the variance components structure.

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=TRUE | FALSE

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

effects={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

noConvert=TRUE | FALSE

when set to True, do not convert the repeated model to a simple one.

Default	FALSE

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

order=integer

specifies the order of covariance structure.

printR={integer-1 <, integer-2, ...>}

displays the blocks of the estimated R matrix.

printRC={integer-1 <, integer-2, ...>}

displays the Cholesky root of the estimated R matrix.

printRCI={integer-1 <, integer-2, ...>}

displays the inverse of the Cholesky root of the estimated R matrix.

printRCorr={integer-1 <, integer-2, ...>}

displays the correlation matrix that corresponds to the estimated R matrix.

printRI={integer-1 <, integer-2, ...>}

displays the inverse of the blocks of the estimated R matrix.

subject={{effect-1} <, {effect-2}, ...>}

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

trial="variable-name"

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

simpleStat=TRUE | FALSE

when set to True, displays the Descriptive Statistics table.

Default	FALSE

singChol=double

tunes the singularity criterion for Cholesky decompositions.

Range	0–1

singRes=double

tunes the singularity criterion for the residual variance.

Range	0–1

singular=double

tunes the general singularity criterion.

Range	0–1

slice={{sliceStatement-1} <, {sliceStatement-2}, ...>}

specifies the effects and related parameters for a partitioned analysis of the LS-Means for an interaction.

* statements={{sliceList-1} <, {sliceList-2}, ...>}

The sliceList value can be one or more of the following:

adjust={method="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

confLimits=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	FALSE

controlLevel={"string-1" <, "string-2", ...>}

displays the differences with a control level of the specified least squares means effects.

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

fTest=TRUE | FALSE

when set to True, requests the F test for testing the mutual equality of the estimable functions in the partitioned analysis of LS-Means.

Default	TRUE

lines=TRUE | FALSE

produces 'Lines' display for pairwise LS-Means difference.

Default	FALSE

means=TRUE | FALSE

specifies to use the covariates means in the partitioned analysis of LS-Means.

Default	FALSE

oddsRatio=TRUE | FALSE

reports differences of LS-Means in terms of odds ratios by the link function.

Default	FALSE

printCoef=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	FALSE

printCorr=TRUE | FALSE

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	FALSE

printCov=TRUE | FALSE

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

sliceBy={{slicebyDef-1} <, {slicebyDef-2}, ...>}

specifies effects by which to partition interaction LSMEANS effects.

The slicebyDef value can be one or more of the following:

levels={"string-1" <, "string-2", ...>}

* term={effect}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* term={effect}

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

spline={{spline-1} <, {spline-2}, ...>}

expands variables into spline bases whose form depends on the specified parameters.

For more information about specifying the spline parameter, see the common spline parameter (Appendix A: Common Parameters).

store={casouttable}

Stores linear mixed models to a blob (binary large object).

Alias	saveState

Long form	store={name="table-name"}
Shortcut form	store="table-name"

The casouttable value can be one or more of the following:

caslib="string"

specifies the name of the caslib for the output table.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

use the default memory format that is set for the server. By default, the server uses the standard memory format. If an administrator sets the CAS_DEFAULT_MEMORY_FORMAT environment variable to DVR, then the DVR memory format is set as the default for the server.

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=TRUE | FALSE

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	FALSE

replace=TRUE | FALSE

when set to True, overwrites an existing table that has the same name.

Default	FALSE

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

test={{testStatement-1} <, {testStatement-2}, ...>}

specifies the effects and related parameters for hypothesis test of fixed effects.

* statements={{testList-1} <, {testList-2}, ...>}

The testList value can be one or more of the following:

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

df=double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

eType={integer-1 <, integer-2, ...>}

specifies the type of coefficients to display.

hType={integer-1 <, integer-2, ...>}

specifies the type of hypothesis test to perform on the specified effects.

Default	3

* terms={{effect-1} <, {effect-2}, ...>}

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

timing=TRUE | FALSE

when set to True, displays the Timing table.

Default	FALSE

weight="variable-name"

names the numeric variable to use in performing a weighted analysis of the data.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NELSON"

No parameters apply when you specify NELSON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

mixed Action

Fits linear mixed models.

Lua Syntax
Summary: Input and Output Tables
Parameter Descriptions

Lua Syntax

results, info = s:mixed_mixed{

blup={

maxIter=double,

outData={

caslib="string"

compress=true | false

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=true | false

replace=true | false

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

solver="DIRECT" | "IOC" | "IOD",

tol=double

byLimit=64-bit-integer,

byOp=integer,

class={{

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string",

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

classglobalopts={

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string"

classLevelsPrint=true | false,

collection={{

details=true | false,

name="string",

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

display={

caseSensitive=true | false,

exclude=true | false,

excludeAll=true | false,

keyIsPath=true | false,

names={"string-1" <, "string-2", ...>},

pathType="LABEL" | "NAME",

traceNames=true | false

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod},

estimate={{

adjSimACC=double,

adjSimCV=true | false,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=true | false,

adjSimSeed=64-bit-integer,

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustNONE} | {airMCAdjustSCHEFFE} | {airMCAdjustSIDAK} | {airMCAdjustSIMULATE} | {airMCAdjustT},

alpha=double,

chiSq=true | false,

cl=true | false,

df=double,

divisor={double-1 <, double-2, ...>},

e=true | false,

group={{

levelIndicator={double-1 <, double-2, ...>},

LMatrixValue=double

}, {...}},

joint=true | false,

lower=true | false,

singular=double,

statements={{

adjSimACC=double,

adjSimCV=true | false,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=true | false,

adjSimSeed=64-bit-integer,

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE},

alpha=double,

chiSq=true | false,

cl=true | false,

df=double,

divisor={double-1 <, double-2, ...>},

e=true | false,

effectCoeff={{

coefficients={{coeffEntry-1} <, {coeffEntry-2}, ...>},

terms={effect}

}, {...}},

group={{coeffEntry-1} <, {coeffEntry-2}, ...>},

label="string",

lower=true | false,

singular=double,

subject={{coeffEntry-1} <, {coeffEntry-2}, ...>},

upper=true | false

}, {...}},

subject={{

levelIndicator={double-1 <, double-2, ...>},

LMatrixValue=double

}, {...}},

upper=true | false

}, {...}},

freq="variable-name",

itDetails=true | false,

lsmeans={{

statements={{

adjSimACC=double,

adjSimCV=true | false,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=true | false,

adjSimSeed=64-bit-integer,

alpha=double,

at="MEANS" | {lsmeansOptionAt},

cl=true | false,

controlLevel={"string-1" <, "string-2", ...>},

corr=true | false,

cov=true | false,

df=double,

e=true | false,

singular=double,

slice={{effect-1} <, {effect-2}, ...>},

terms={{effect-1} <, {effect-2}, ...>} | {"string-1" <, "string-2", ...>}

}, {...}}

}, {...}},

lsmestimate={{

statements={{

adjust={method="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

byLevel=true | false,

chiSq=true | false,

coeff={{namedCoeffDef-1} <, {namedCoeffDef-2}, ...>},

confLimits=true | false,

df=double,

divisor={double-1 <, double-2, ...>},

joint=true | false,

lower=true | false,

obsMargins=true | false,

obsMarginsData={castable},

printCoef=true | false,

printCorr=true | false,

printCov=true | false,

printKCoef=true | false,

singular=double,

terms={{effect-1} <, {effect-2}, ...>},

upper=true | false

}, {...}}

}, {...}},

margins={{

statements={{

adjust={method="BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

byLevel=true | false,

chiSq=true | false,

confLimits=true | false,

controlLevel={"string-1" <, "string-2", ...>},

df=double,

joint=true | false,

lines=true | false,

obsMargins=true | false,

obsMarginsData={castable},

odds=true | false,

oddsRatio=true | false,

printCoef=true | false,

singular=double,

slice={{effect-1} <, {effect-2}, ...>},

sliceControlLevel={"string-1" <, "string-2", ...>},

terms={{effect-1} <, {effect-2}, ...>},

weight=true | false

}, {...}}

}, {...}},

maxClPrint=integer,

method="MIVQUE0" | "ML" | "REML",

mmeq=true | false,

model={

alpha=double,

cl=true | false,

ddf={double-1 <, double-2, ...>},

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

dfMethod="CONTAIN" | "NONE" | "RESIDUAL",

effects={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

noint=true | false,

offset="variable-name",

printDenseSol=true | false,

printSol=true | false,

trial="variable-name",

zeta=double

multimember={{

details=true | false,

name="string",

noEffect=true | false,

stdize=true | false,

vars={"variable-name-1" <, "variable-name-2", ...>},

weight={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

nClassLevelsPrint=integer,

noBlupVar=true | false,

noBound=true | false,

noClPrint=integer,

noInfo=true | false,

noItPrint=true | false,

noPrint=true | false,

noProfile=true | false,

optimization={

absConv=double,

absConvNum=integer,

absFConv=double,

absFConvNum=integer,

absGConv=double,

absGConvNum=integer,

absXConv=double,

absXConvNum=integer,

fConv=double,

fConv2=double,

fConvNum=integer,

fSize=double,

gConv=double,

gConv2=double,

gConvNum=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

optTol=double,

xConv=double,

xConvNum=integer,

xSize=double

output={

allStats=true | false,

alpha=double,

casOut={

caslib="string"

compress=true | false

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=true | false

replace=true | false

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>},

lcl="string",

lclPA="string",

noMiss=true | false,

pearson="string",

pearsonPA="string",

pred="string",

predPA="string",

resid="string",

residPA="string",

stderr="string",

stderrPA="string",

student="string",

studentPA="string",

ucl="string",

uclPA="string",

variance="string",

variancePA="string"

outputTables={

groupByVarsRaw=true | false,

includeAll=true | false,

names={"string-1" <, "string-2", ...>} | {key-1={casouttable-1} <, key-2={casouttable-2}, ...>},

repeated=true | false,

replace=true | false

parms={

hold={integer-1 <, integer-2, ...>},

holdAll=true | false,

initvals={{list-1} <,{list-2}, ...>},

lowerB={double-1 <, double-2, ...>},

noIter=true | false,

parmsData={

caslib="string"

computedOnDemand=true | false

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

computedVarsProgram="string"

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

groupByMode="NOSORT" | "REDISTRIBUTE"

name="table-name"

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

singlePass=true | false

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

residVar=double,

upperB={double-1 <, double-2, ...>}

polynomial={{

degree=integer,

details=true | false,

labelStyle={

expand=true | false

exponent="string"

includeName=true | false

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=true | false,

standardize={

method="MOMENTS" | "MRANGE" | "WMOMENTS"

options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

prefix="NONE" | "string"

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

random={{

alpha=double,

cl=true | false,

covType="ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC",

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

effects={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

group={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

noint=true | false,

order=integer,

printSol=true | false,

subject={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

trial="variable-name"

}, {...}},

ranks=true | false,

repeated={{

covType="AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC",

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

effects={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

group={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

noConvert=true | false,

noint=true | false,

order=integer,

printR={integer-1 <, integer-2, ...>},

printRC={integer-1 <, integer-2, ...>},

printRCI={integer-1 <, integer-2, ...>},

printRCorr={integer-1 <, integer-2, ...>},

printRI={integer-1 <, integer-2, ...>},

subject={{

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest={"string-1" <, "string-2", ...>},

vars={"string-1" <, "string-2", ...>}

}, {...}},

trial="variable-name"

}, {...}},

seed=64-bit-integer,

simpleStat=true | false,

singChol=double,

singRes=double,

singular=double,

slice={{

statements={{

adjust={method="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters},

alpha=double,

at="MEANS" | {lsmeansOptionAt},

confLimits=true | false,

controlLevel={"string-1" <, "string-2", ...>},

df=double,

fTest=true | false,

lines=true | false,

means=true | false,

oddsRatio=true | false,

printCoef=true | false,

printCorr=true | false,

printCov=true | false,

singular=double,

sliceBy={{

levels={"string-1" <, "string-2", ...>},

term={effect}

}, {...}},

term={effect}

}, {...}}

}, {...}},

spline={{

basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

dataBoundary=true | false,

degree=integer,

details=true | false,

knotMax=double,

knotMethod={

equal=integer

list={double-1 <, double-2, ...>}

listWithBoundary={double-1 <, double-2, ...>}

multiscale={

endScale=integer

startScale=integer

}

rangeFractions={double-1 <, double-2, ...>}

knotMin=double,

name="string",

naturalCubic=true | false,

separate=true | false,

split=true | false,

vars={"variable-name-1" <, "variable-name-2", ...>}

}, {...}},

store={

caslib="string",

label="string",

lifetime=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=true | false,

replace=true | false,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

table={

caslib="string",

computedOnDemand=true | false,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=true | false,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

test={{

statements={{

chiSq=true | false,

df=double,

eType={integer-1 <, integer-2, ...>},

hType={integer-1 <, integer-2, ...>},

terms={{effect-1} <, {effect-2}, ...>}

}, {...}}

}, {...}},

timing=true | false,

weight="variable-name"

}

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
lsmestimate	required parameterstatements (and nested parameter obsMarginsData)	specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.
margins	required parameterstatements (and nested parameter obsMarginsData)	specifies the effects and related parameters for predictive margins of fixed effects.
parms	parmsData	specifies the initial covariance values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
blup	required parameteroutData	creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	Stores linear mixed models to a blob (binary large object).

Parameter Descriptions

blup={mixedBlupStmt}

creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.

The mixedBlupStmt value can be one or more of the following:

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

* outData={casouttable}

names the table on the server that contains BLUE and BLUP values.

For more information about specifying the outData parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

solver="DIRECT" | "IOC" | "IOD"

names the solver that is used to obtain the BLUE and BLUP.

Default	IOC

DIRECT

requires storing mixed model equations (MMEq) in memory and computing the Cholesky decomposition of MMEq.

IOC

requires storing mixed model equations (MMEq) in memory and iterates on MMEq to solve for the solutions.

IOD

does not build mixed model equations; instead it iterates on data to solve for the solutions.

tol=double

specifies the convergence criteria for solving the BLUP by the iteration method.

Alias	tolerance
Minimum value	0

byLimit=64-bit-integer

specifies that the analysis will not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

byOp=integer

specifies the method to use for BY-group processing.

Default	0
Range	0–1

class={{classStatement-1} <, {classStatement-2}, ...>}

names the classification variables to be used as explanatory variables in the analysis.

Aliases	classVars
Aliases	nominal

The classStatement value can be one or more of the following:

countMissing=true | false

when set to True, treats missing as a valid level for this variable.

Default	false

descending=true | false

when set to True, reverses the sort order that is imposed by the order parameter.

Default	false

ignoreMissing=true | false

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	false

levelizeRaw=true | false

when set to True, bases levelization for this variable on raw values.

Default	false

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

ref="FIRST" | "LAST" | double | "string"

* vars={"variable-name-1" <, "variable-name-2", ...>}

specifies the classification variables.

Alias	name

classglobalopts={classopts}

lists options that apply to all classification variables.

Long form	classglobalopts={param="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"}
Shortcut form	classglobalopts="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

The classopts value can be one or more of the following:

countMissing=true | false

when set to True, treats missing as a valid level for this variable.

Default	false

descending=true | false

when set to True, reverses the sort order that is imposed by the order parameter.

Default	false

ignoreMissing=true | false

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	false

levelizeRaw=true | false

when set to True, bases levelization for this variable on raw values.

Default	false

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

For more information, see the description of the order subparameter in the class parameter (Shared Concepts).

param="EFFECT" | "GLM" | "ORDINAL" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

For more information, see the description of the param subparameter in the class parameter (Shared Concepts).

ref="FIRST" | "LAST" | double | "string"

classLevelsPrint=true | false

when set to False, suppresses the display of class levels.

Default	true

collection={{collection-1} <, {collection-2}, ...>}

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

The collection value can be one or more of the following:

details=true | false

when set to True, requests a table that shows additional details that are related to this effect.

Default	false

* name="string"

specifies the name of the effect.

* vars={"variable-name-1" <, "variable-name-2", ...>}

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod}

specifies the design matrix method.

The mixedDmMethod value can be one or more of the following:

* method="DENSE" | "NOSPEC" | "SPARSE"

smmethod="BASIC" | "NDORDERING"

estimate={{estimateStmt-1} <, {estimateStmt-2}, ...>}

specifies the effects, their coefficients, and the options for a customized linear estimation.

The estimateStmt value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

adjSimCV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

adjSimReport=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

chiSq=true | false

requests a chi-square test in addition to the F test.

Default	false

cl=true | false

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	false

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor={double-1 <, double-2, ...>}

specifies a list of values to divide the coefficients.

e=true | false

when set to True, displays the matrix coefficients for all effects.

Default	false

group={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

joint=true | false

requests a joint test for the LS-Means.

Default	false

lower=true | false

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	false

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

* statements={{estimateList-1} <, {estimateList-2}, ...>}

The estimateList value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

adjSimCV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

adjSimReport=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

chiSq=true | false

requests a chi-square test in addition to the F test.

Default	false

cl=true | false

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	false

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor={double-1 <, double-2, ...>}

specifies a list of values to divide the coefficients.

e=true | false

when set to True, displays the matrix coefficients for all effects.

Default	false

* effectCoeff={{coeffDefinition-1} <, {coeffDefinition-2}, ...>}

specifies an effect and its non-positional coefficients.

The coeffDefinition value can be one or more of the following:

coefficients={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

* terms={effect}

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

* label="string"

specifies a name for every row of the multirow estimate.

Alias	name

lower=true | false

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	false

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

subject={{coeffEntry-1} <, {coeffEntry-2}, ...>}

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

upper=true | false

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	false

subject={{coeffEntry-1} <, {coeffEntry-2}, ...>}

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

upper=true | false

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	false

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

itDetails=true | false

when set to True, adds the covariance values to the iteration history at each step of the optimization.

Default	false

lsmeans={{lsmeansStatement-1} <, {lsmeansStatement-2}, ...>}

specifies the effects and related parameters for least squares means of fixed effects.

* statements={{lsmeansList-1} <, {lsmeansList-2}, ...>}

The lsmeansList value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

adjSimCV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01
Range	0–1

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604
Minimum value	0

adjSimReport=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY" | {airMCAdjustTUKEY} | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSMM} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustDUNNETT} | {airMCAdjustNELSON} | {airMCAdjustT} | {airMCAdjustNONE}

determines the adjustment method for multiple comparisons of LS-Means differences.

The airMCAdjustTUKEY value is specified as follows:

* method="TUKEY"

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSMM value is specified as follows:

* method="GT2" | "SMM"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustDUNNETT value is specified as follows:

* method="DUNNETT"

The airMCAdjustNELSON value is specified as follows:

* method="NELSON"

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

cl=true | false

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	false

controlLevel={"string-1" <, "string-2", ...>}

displays the differences with a control level of the specified least squares means effects.

corr=true | false

when set to True, displays the estimated correlation matrix of the least squares means.

Default	false

cov=true | false

when set to True, displays the estimated covariance matrix of the least squares means.

Default	false

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

e=true | false

when set to True, displays the matrix coefficients for all effects.

Default	false

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

slice={{effect-1} <, {effect-2}, ...>}

specifies effects by which to partition interaction LSMEANS effects.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* terms={{effect-1} <, {effect-2}, ...>} | {"string-1" <, "string-2", ...>}

specifies effects in the model for the estimates of the least squares means.

The effect value is specified as follows:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

lsmestimate={{lsmestimateStatement-1} <, {lsmestimateStatement-2}, ...>}

specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.

* statements={{lsmestimateList-1} <, {lsmestimateList-2}, ...>}

The lsmestimateList value can be one or more of the following:

adjust={method="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

byLevel=true | false

computes separate margins.

Default	false

chiSq=true | false

requests a chi-square test in addition to the F test.

Default	false

coeff={{namedCoeffDef-1} <, {namedCoeffDef-2}, ...>}

The namedCoeffDef value can be one or more of the following:

coefficients={{coeffEntry-1} <, {coeffEntry-2}, ...>}

The coeffEntry value can be one or more of the following:

levelIndicator={double-1 <, double-2, ...>}

* LMatrixValue=double

divisor=double

specifies a list of values to divide the coefficients.

* name="string"

specifies a name for every row of the multirow estimate.

confLimits=true | false

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	false

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor={double-1 <, double-2, ...>}

specifies a list of values to divide the coefficients.

joint=true | false

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Default	false

lower=true | false

performs one-sided, lower-tailed inference.

Alias	lowerTailed
Default	false

obsMargins=true | false

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	false

obsMarginsData={castable}

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=true | false

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	false

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

orderBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

singlePass=true | false

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	false

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

printCoef=true | false

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	false

printCorr=true | false

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	false

printCov=true | false

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	false

printKCoef=true | false

when set to True, displays the K matrix coefficients for the specified effects.

Alias	elsm
Default	false

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

* terms={{effect-1} <, {effect-2}, ...>}

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

upper=true | false

performs one-sided, upper-tailed inference.

Alias	upperTailed
Default	false

margins={{marginsStatement-1} <, {marginsStatement-2}, ...>}

specifies the effects and related parameters for predictive margins of fixed effects.

* statements={{marginsList-1} <, {marginsList-2}, ...>}

The marginsList value can be one or more of the following:

adjust={method="BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

byLevel=true | false

computes separate margins.

Default	false

chiSq=true | false

requests a chi-square test in addition to the F test.

Default	false

confLimits=true | false

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	false

controlLevel={"string-1" <, "string-2", ...>}

displays the differences with a control level of the specified least squares means effects.

df=double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

joint=true | false

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Alias	fTest
Default	false

lines=true | false

produces 'Lines' display for pairwise LS-Means difference.

Default	false

obsMargins=true | false

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	false

obsMarginsData={castable}

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=true | false

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	false

computedVars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

groupBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

orderBy={{casinvardesc-1} <, {casinvardesc-2}, ...>}

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

singlePass=true | false

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	false

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable={groupbytable}

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

odds=true | false

reports odds of levels of fixed effects if permissible by the link function.

Default	false

oddsRatio=true | false

reports differences of LS-Means in terms of odds ratios by the link function.

Default	false

printCoef=true | false

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	false

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

slice={{effect-1} <, {effect-2}, ...>}

specifies effects by which to partition interaction LSMEANS effects.

Alias	sliceBy

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

sliceControlLevel={"string-1" <, "string-2", ...>}

requests slice effects differences with a control level of each of the specified LSMEANS effects.

sliceDiff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

determines the type of simple effects differences.

Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

* terms={{effect-1} <, {effect-2}, ...>}

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

weight=true | false

when set to True, computes the weighted predictive margins.

Default	false

maxClPrint=integer

specifies the maximum levels of classification variables to print in the ClassLevels table.

Default	20

method="MIVQUE0" | "ML" | "REML"

specifies the estimation method for covariance estimation analysis.

Default	REML

MIVQUE0

performs minimum variance quadratic unbiased estimation (MIVQUE0).

ML

performs maximum likelihood estimation (ML).

REML

performs residual (restricted) maximum likelihood estimation (REML).

mmeq=true | false

when set to True, displays the mixed model equations table.

Default	false

* model={mixedModelStmt}

names the dependent variable, explanatory effects, and model options.

The mixedModelStmt value can be one or more of the following:

alpha=double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

cl=true | false

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	false

ddf={double-1 <, double-2, ...>}

specifies a list of the customized denominator degrees of freedom for the fixed effects.

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=true | false

Default	false

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

dfMethod="CONTAIN" | "NONE" | "RESIDUAL"

specifies the degrees of freedom method.

Alias	ddfm
Default	RESIDUAL

dist="BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL"

specifies the response distribution for the model.

effects={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

link="CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO"

specifies the link function for the model.

noint=true | false

when set to True, does not include the intercept term in the model.

Default	false

offset="variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

printDenseSol=true | false

when set to True, displays the fixed effects estimates.

Default	false

printSol=true | false

when set to True, displays the fixed effects estimates.

Default	false

trial="variable-name"

zeta=double

specifies a value to tune the estimability check.

Range	0–1

multimember={{multimember-1} <, {multimember-2}, ...>}

For more information about specifying the multimember parameter, see the common multimember parameter (Appendix A: Common Parameters).

nClassLevelsPrint=integer

limits the display of class levels. The value 0 suppresses all levels.

Minimum value	0

noBlupVar=true | false

when set to True, suppresses computing the covariances of random-effects estimates that are used in output statistics.

Default	false

noBound=true | false

when set to True, enforces no boundary restriction for estimating covariance parameters.

Default	false

noClPrint=integer

Default	0

noInfo=true | false

when set to True, suppresses the display of the Model Information, Number of Observations, and Dimensions tables.

Default	false

noItPrint=true | false

when set to True, suppresses the display of the Iteration History table.

Default	false

noPrint=true | false

when set to True, suppresses the display of results.

Default	false

noProfile=true | false

when set to True, includes the residual variance as one of the covariance values in the optimization iterations.

Default	false

optimization={mixedOptimizationStmt}

specifies the technique and options for performing the optimization.

Long form	optimization={technique="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"}
Shortcut form	optimization="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"

The mixedOptimizationStmt value can be one or more of the following:

absConv=double

specifies the absolute function convergence criterion.

Minimum value	0

absConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absFConv=double

specifies the absolute function difference convergence criterion.

Minimum value	0

absFConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Minimum value	0

absGConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absXConv=double

specifies the absolute convergence criterion.

Minimum value	0

absXConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Minimum value	0

fConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

fSize=double

specifies the value to use for both the relative function convergence criterion and the relative gradient convergence criterion.

Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Minimum value	0

gConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Default	200
Minimum value	0

maxTime=double

specifies the maximum allowed computing time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

optTol=double

defines the measure by which you can decide whether the value in the current iteration is an acceptable approximation of a local minimum.

Default	1E-05
Minimum value	0

technique="ACTIVESET" | "CONGRA" | "DBLDOG" | "IPDIRECT" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "QUANEW" | "TRUREG"

specifies the optimization technique.

ACTIVESET

uses the active set method.

CONGRA

uses the conjugate gradient method, which requires first-order derivatives.

DBLDOG

uses the double-dogleg method, which requires first-order derivatives.

IPDIRECT

uses the direct interior point method.

LBFGS

uses the Limited-memory BFGS solver, which requires first-order derivatives.

NEWRAP

uses the Newton-Raphson method with line search and ridging, which requires first- and second-order derivatives.

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed using the initial covariance values.

NRRIDG

uses the Newton-Raphson method with ridging, which requires first- and second-order derivatives.

QUANEW

uses the dual quasi-Newton method, which requires first-order derivatives.

TRUREG

uses the trust region method, which requires first- and second-order derivatives.

xConv=double

specifies the relative convergence criterion.

Minimum value	0

xConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

xSize=double

specifies the value to use for the relative convergence criterion.

Minimum value	0

output={mixedOutputStmt}

creates a table on the server that contains observationwise statistics, which are computed after fitting the model.

The mixedOutputStmt value can be one or more of the following:

allStats=true | false

when set to True, requests all available statistics.

Default	false

alpha=double

specifies the significance level to use in output statistics. The default value is 0.05.

Default	0.05
Range	0–1

* casOut={casouttable}

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>}

lcl="string"

names the lower bound of a confidence interval for the linear predictor.

lclPA="string"

names the lower bound of a confidence interval for the marginal linear predictor.

noMiss=true | false

when set to True, writes only observations that are used in the analysis to the output data table. By default, output statistics for all observations are written to the output data table.

Default	false

pearson="string"

names the Pearson-type residual.

pearsonPA="string"

names the marginal Pearson-type residual.

pred="string"

names the linear predictor. If no output statistics are specified, then this is the default.

Aliases	p
Aliases	predicted

predPA="string"

names the marginal linear predictor.

resid="string"

names the residual, which is calculated as ACTUAL minus PREDICTED.

Aliases	r
Aliases	residual

residPA="string"

names the marginal standard deviation of the linear predictor.

stderr="string"

names the standard deviation of the linear predictor.

stderrPA="string"

names the marginal standard deviation of the linear predictor.

student="string"

names the studentized residuals, which are the residuals divided by their standard errors.

studentPA="string"

names the marginal residual.

ucl="string"

names the upper bound of a confidence interval for the linear predictor.

uclPA="string"

names the upper bound of a confidence interval for the marginal linear predictor.

variance="string"

names the conditional variance of the response variable.

variancePA="string"

names the marginal variance of the response variable.

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	displayOut

parms={mixedParmsStmt}

specifies the initial covariance values.

The mixedParmsStmt value can be one or more of the following:

hold={integer-1 <, integer-2, ...>}

holds all or partial covariance values.

Alias	eqcons

holdAll=true | false

when set to True, holds all covariance values.

Default	false

initvals={{list-1} <,{list-2}, ...>}

specifies the initial covariance values.

lowerB={double-1 <, double-2, ...>}

specifies the lower boundary for covariance values.

noIter=true | false

when set to True, performs no iteration for estimating covariance parameters.

Default	false

parmsData={castable}

names the data table that contains the initial covariance values.

For more information about specifying the parmsData parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

Alias	pData

residVar=double

specifies a value for residual variance and excludes it from optimization search.

Minimum value	1E-08

upperB={double-1 <, double-2, ...>}

specifies the upper boundary for covariance values.

polynomial={{polynomial-1} <, {polynomial-2}, ...>}

For more information about specifying the polynomial parameter, see the common polynomial parameter (Appendix A: Common Parameters).

Alias	poly

random={{mixedRandomStmt-1} <, {mixedRandomStmt-2}, ...>}

specifies random effects and related options. Notice that no-intercept is inherited from the model specification where default value is FALSE, but the default value in random specification is TRUE.

The mixedRandomStmt value can be one or more of the following:

alpha=double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

cl=true | false

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	false

covType="ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC"

specifies the type of covariance structure.

Default	VC

ANTE

specifies the first-order antedependence covariance structure.

AR

specifies the first-order autoregressive covariance structure.

ARH

specifies the heterogeneous first-order autoregressive covariance structure.

ARMA

specifies the autoregressive moving average covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

FA

specifies the factor-analytic covariance structure.

FA0

specifies the no-diagonal factor-analytic covariance structure.

FA1

specifies the equal-diagonal factor-analytic covariance structure.

HF

specifies the Huynh-Feldt covariance structure.

TOEP

specifies the Toeplitz covariance structure.

TOEPH

specifies the heterogeneous Toeplitz covariance structure.

UC

specifies the uniform correlation covariance structure.

UN

specifies the unstructured covariance.

UNR

specifies the unstructured correlation covariance structure.

VC

specifies the variance components structure.

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=true | false

Default	false

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

effects={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

noint=true | false

when set to True, does not include the intercept term in the model.

Default	false

order=integer

specifies the order of covariance structure.

printSol=true | false

when set to True, displays the random-effects estimates. You can set this parameter to True by specifying the alpha or cl parameters in the model specification.

Default	false

subject={{effect-1} <, {effect-2}, ...>}

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

trial="variable-name"

ranks=true | false

when set to True, displays the table of ranks of the matrices X, (XZ), and MMEq.

Default	false

repeated={{mixedRepeatedStmt-1} <, {mixedRepeatedStmt-2}, ...>}

The mixedRepeatedStmt value can be one or more of the following:

covType="AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC"

specifies the type of covariance structure.

Default	VC

AR

specifies the first-order autoregressive covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

UN

specifies the unstructured covariance.

VC

specifies the variance components structure.

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=true | false

Default	false

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

effects={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group={{effect-1} <, {effect-2}, ...>}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

noConvert=true | false

when set to True, do not convert the repeated model to a simple one.

Default	false

noint=true | false

when set to True, does not include the intercept term in the model.

Default	false

order=integer

specifies the order of covariance structure.

printR={integer-1 <, integer-2, ...>}

displays the blocks of the estimated R matrix.

printRC={integer-1 <, integer-2, ...>}

displays the Cholesky root of the estimated R matrix.

printRCI={integer-1 <, integer-2, ...>}

displays the inverse of the Cholesky root of the estimated R matrix.

printRCorr={integer-1 <, integer-2, ...>}

displays the correlation matrix that corresponds to the estimated R matrix.

printRI={integer-1 <, integer-2, ...>}

displays the inverse of the blocks of the estimated R matrix.

subject={{effect-1} <, {effect-2}, ...>}

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

trial="variable-name"

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

simpleStat=true | false

when set to True, displays the Descriptive Statistics table.

Default	false

singChol=double

tunes the singularity criterion for Cholesky decompositions.

Range	0–1

singRes=double

tunes the singularity criterion for the residual variance.

Range	0–1

singular=double

tunes the general singularity criterion.

Range	0–1

slice={{sliceStatement-1} <, {sliceStatement-2}, ...>}

specifies the effects and related parameters for a partitioned analysis of the LS-Means for an interaction.

* statements={{sliceList-1} <, {sliceList-2}, ...>}

The sliceList value can be one or more of the following:

adjust={method="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | {double-1 <, double-2, ...>}

sets values of covariates.

* vars="string" | {"string-1" <, "string-2", ...>}

sets names of covariates.

confLimits=true | false

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	false

controlLevel={"string-1" <, "string-2", ...>}

displays the differences with a control level of the specified least squares means effects.

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

fTest=true | false

when set to True, requests the F test for testing the mutual equality of the estimable functions in the partitioned analysis of LS-Means.

Default	true

lines=true | false

produces 'Lines' display for pairwise LS-Means difference.

Default	false

means=true | false

specifies to use the covariates means in the partitioned analysis of LS-Means.

Default	false

oddsRatio=true | false

reports differences of LS-Means in terms of odds ratios by the link function.

Default	false

printCoef=true | false

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	false

printCorr=true | false

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	false

printCov=true | false

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	false

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

sliceBy={{slicebyDef-1} <, {slicebyDef-2}, ...>}

specifies effects by which to partition interaction LSMEANS effects.

The slicebyDef value can be one or more of the following:

levels={"string-1" <, "string-2", ...>}

* term={effect}

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* term={effect}

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

spline={{spline-1} <, {spline-2}, ...>}

expands variables into spline bases whose form depends on the specified parameters.

For more information about specifying the spline parameter, see the common spline parameter (Appendix A: Common Parameters).

store={casouttable}

Stores linear mixed models to a blob (binary large object).

Alias	saveState

Long form	store={name="table-name"}
Shortcut form	store="table-name"

The casouttable value can be one or more of the following:

caslib="string"

specifies the name of the caslib for the output table.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=true | false

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	false

replace=true | false

when set to True, overwrites an existing table that has the same name.

Default	false

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

test={{testStatement-1} <, {testStatement-2}, ...>}

specifies the effects and related parameters for hypothesis test of fixed effects.

* statements={{testList-1} <, {testList-2}, ...>}

The testList value can be one or more of the following:

chiSq=true | false

requests a chi-square test in addition to the F test.

Default	false

df=double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

eType={integer-1 <, integer-2, ...>}

specifies the type of coefficients to display.

hType={integer-1 <, integer-2, ...>}

specifies the type of hypothesis test to perform on the specified effects.

Default	3

* terms={{effect-1} <, {effect-2}, ...>}

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest={"string-1" <, "string-2", ...>}

* vars={"string-1" <, "string-2", ...>}

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

timing=true | false

when set to True, displays the Timing table.

Default	false

weight="variable-name"

names the numeric variable to use in performing a weighted analysis of the data.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NELSON"

No parameters apply when you specify NELSON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=true | false

specifies CV option in ADJUST=SIMULATE.

Default	false

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=true | false

specifies REPORT option in ADJUST=SIMULATE.

Default	false

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

mixed Action

Fits linear mixed models.

Python Syntax
Summary: Input and Output Tables
Parameter Descriptions

Python Syntax

results=s.mixed.mixed(

blup={

"maxIter":double,

"outData":{

"caslib":"string"

"compress":True | False

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

"name":"table-name"

"promote":True | False

"replace":True | False

"replication":integer

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

"solver":"DIRECT" | "IOC" | "IOD",

"tol":double

byLimit=64-bit-integer,

byOp=integer,

class_=[{

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

"ref":"FIRST" | "LAST" | double | "string",

"vars":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>],

classglobalopts={

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

"ref":"FIRST" | "LAST" | double | "string"

classLevelsPrint=True | False,

collection=[{

"details":True | False,

"name":"string",

"vars":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>],

display={

"caseSensitive":True | False,

"exclude":True | False,

"excludeAll":True | False,

"keyIsPath":True | False,

"names":["string-1" <, "string-2", ...>],

"pathType":"LABEL" | "NAME",

"traceNames":True | False

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod},

estimate=[{

"adjSimACC":double,

"adjSimCV":True | False,

"adjSimEPS":double,

"adjSimNSamp":64-bit-integer,

"adjSimReport":True | False,

"adjSimSeed":64-bit-integer,

"adjust":"BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustNONE} | {airMCAdjustSCHEFFE} | {airMCAdjustSIDAK} | {airMCAdjustSIMULATE} | {airMCAdjustT},

"alpha":double,

"chiSq":True | False,

"cl":True | False,

"df":double,

"divisor":[double-1 <, double-2, ...>],

"e":True | False,

"group":[{

"levelIndicator":[double-1 <, double-2, ...>],

"LMatrixValue":double

}<, {...}>],

"joint":True | False,

"lower":True | False,

"singular":double,

"statements":[{

"adjSimACC":double,

"adjSimCV":True | False,

"adjSimEPS":double,

"adjSimNSamp":64-bit-integer,

"adjSimReport":True | False,

"adjSimSeed":64-bit-integer,

"adjust":"BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE},

"alpha":double,

"chiSq":True | False,

"cl":True | False,

"df":double,

"divisor":[double-1 <, double-2, ...>],

"e":True | False,

"effectCoeff":[{

coefficients=[{coeffEntry-1} <, {coeffEntry-2}, ...>],

terms={effect}

}<, {...}>],

"group":[{coeffEntry-1} <, {coeffEntry-2}, ...>],

"label":"string",

"lower":True | False,

"singular":double,

"subject":[{coeffEntry-1} <, {coeffEntry-2}, ...>],

"upper":True | False

}<, {...}>],

"subject":[{

"levelIndicator":[double-1 <, double-2, ...>],

"LMatrixValue":double

}<, {...}>],

"upper":True | False

}<, {...}>],

freq="variable-name",

itDetails=True | False,

lsmeans=[{

"statements":[{

"adjSimACC":double,

"adjSimCV":True | False,

"adjSimEPS":double,

"adjSimNSamp":64-bit-integer,

"adjSimReport":True | False,

"adjSimSeed":64-bit-integer,

"adjust":"BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY" | {airMCAdjustTUKEY} | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSMM} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustDUNNETT} | {airMCAdjustNELSON} | {airMCAdjustT} | {airMCAdjustNONE},

"alpha":double,

"at":"MEANS" | {lsmeansOptionAt},

"cl":True | False,

"controlLevel":["string-1" <, "string-2", ...>],

"corr":True | False,

"cov":True | False,

"df":double,

"e":True | False,

"singular":double,

"slice":[{effect-1} <, {effect-2}, ...>],

"terms":[{effect-1} <, {effect-2}, ...>] | ["string-1" <, "string-2", ...>]

}<, {...}>]

}<, {...}>],

lsmestimate=[{

"statements":[{

"adjust":{"method":"BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters},

"alpha":double,

"at":"MEANS" | {lsmeansOptionAt},

"byLevel":True | False,

"chiSq":True | False,

"coeff":[{namedCoeffDef-1} <, {namedCoeffDef-2}, ...>],

"confLimits":True | False,

"df":double,

"divisor":[double-1 <, double-2, ...>],

"joint":True | False,

"lower":True | False,

"obsMargins":True | False,

"obsMarginsData":{castable},

"printCoef":True | False,

"printCorr":True | False,

"printCov":True | False,

"printKCoef":True | False,

"singular":double,

"terms":[{effect-1} <, {effect-2}, ...>],

"upper":True | False

}<, {...}>]

}<, {...}>],

margins=[{

"statements":[{

"adjust":{"method":"BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters},

"alpha":double,

"at":"MEANS" | {lsmeansOptionAt},

"byLevel":True | False,

"chiSq":True | False,

"confLimits":True | False,

"controlLevel":["string-1" <, "string-2", ...>],

"df":double,

"joint":True | False,

"lines":True | False,

"obsMargins":True | False,

"obsMarginsData":{castable},

"odds":True | False,

"oddsRatio":True | False,

"printCoef":True | False,

"singular":double,

"slice":[{effect-1} <, {effect-2}, ...>],

"sliceControlLevel":["string-1" <, "string-2", ...>],

"terms":[{effect-1} <, {effect-2}, ...>],

"weight":True | False

}<, {...}>]

}<, {...}>],

maxClPrint=integer,

method="MIVQUE0" | "ML" | "REML",

mmeq=True | False,

model={

"alpha":double,

"cl":True | False,

"ddf":[double-1 <, double-2, ...>],

"depVars":[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"dfMethod":"CONTAIN" | "NONE" | "RESIDUAL",

"effects":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"noint":True | False,

"offset":"variable-name",

"printDenseSol":True | False,

"printSol":True | False,

"trial":"variable-name",

"zeta":double

multimember=[{

"details":True | False,

"name":"string",

"noEffect":True | False,

"stdize":True | False,

"vars":["variable-name-1" <, "variable-name-2", ...>],

"weight":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>],

nClassLevelsPrint=integer,

noBlupVar=True | False,

noBound=True | False,

noClPrint=integer,

noInfo=True | False,

noItPrint=True | False,

noPrint=True | False,

noProfile=True | False,

optimization={

"absConv":double,

"absConvNum":integer,

"absFConv":double,

"absFConvNum":integer,

"absGConv":double,

"absGConvNum":integer,

"absXConv":double,

"absXConvNum":integer,

"fConv":double,

"fConv2":double,

"fConvNum":integer,

"fSize":double,

"gConv":double,

"gConv2":double,

"gConvNum":integer,

"maxFunc":double,

"maxIter":double,

"maxTime":double,

"minIter":integer,

"optTol":double,

"xConv":double,

"xConvNum":integer,

"xSize":double

output={

"allStats":True | False,

"alpha":double,

"casOut":{

"caslib":"string"

"compress":True | False

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

"name":"table-name"

"promote":True | False

"replace":True | False

"replication":integer

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

"copyVars":"ALL" | "ALL_MODEL" | "ALL_NUMERIC" | ["variable-name-1" <, "variable-name-2", ...>],

"lcl":"string",

"lclPA":"string",

"noMiss":True | False,

"pearson":"string",

"pearsonPA":"string",

"pred":"string",

"predPA":"string",

"resid":"string",

"residPA":"string",

"stderr":"string",

"stderrPA":"string",

"student":"string",

"studentPA":"string",

"ucl":"string",

"uclPA":"string",

"variance":"string",

"variancePA":"string"

outputTables={

"groupByVarsRaw":True | False,

"includeAll":True | False,

"names":["string-1" <, "string-2", ...>] | {"key-1":{casouttable-1} <, "key-2":{casouttable-2}, ...>},

"repeated":True | False,

"replace":True | False

parms={

"hold":[integer-1 <, integer-2, ...>],

"holdAll":True | False,

"initvals":[[list-1] <,[list-2], ...>],

"lowerB":[double-1 <, double-2, ...>],

"noIter":True | False,

"parmsData":{

"caslib":"string"

"computedOnDemand":True | False

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"computedVarsProgram":"string"

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

"groupBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"groupByMode":"NOSORT" | "REDISTRIBUTE"

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

"name":"table-name"

"orderBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"singlePass":True | False

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

"whereTable":{

"casLib":"string"

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

"residVar":double,

"upperB":[double-1 <, double-2, ...>]

polynomial=[{

"degree":integer,

"details":True | False,

"labelStyle":{

"expand":True | False

"exponent":"string"

"includeName":True | False

"productSymbol":"NONE" | "string"

"mDegree":integer,

"name":"string",

"noSeparate":True | False,

"standardize":{

"method":"MOMENTS" | "MRANGE" | "WMOMENTS"

"options":"CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

"prefix":"NONE" | "string"

"vars":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>],

random=[{

"alpha":double,

"cl":True | False,

"covType":"ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC",

"depVars":[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"effects":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"group":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"noint":True | False,

"order":integer,

"printSol":True | False,

"subject":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"trial":"variable-name"

}<, {...}>],

ranks=True | False,

repeated=[{

"covType":"AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC",

"depVars":[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"effects":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"group":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"noConvert":True | False,

"noint":True | False,

"order":integer,

"printR":[integer-1 <, integer-2, ...>],

"printRC":[integer-1 <, integer-2, ...>],

"printRCI":[integer-1 <, integer-2, ...>],

"printRCorr":[integer-1 <, integer-2, ...>],

"printRI":[integer-1 <, integer-2, ...>],

"subject":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"trial":"variable-name"

}<, {...}>],

seed=64-bit-integer,

simpleStat=True | False,

singChol=double,

singRes=double,

singular=double,

slice=[{

"statements":[{

"adjust":{"method":"BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters},

"alpha":double,

"at":"MEANS" | {lsmeansOptionAt},

"confLimits":True | False,

"controlLevel":["string-1" <, "string-2", ...>],

"df":double,

"fTest":True | False,

"lines":True | False,

"means":True | False,

"oddsRatio":True | False,

"printCoef":True | False,

"printCorr":True | False,

"printCov":True | False,

"singular":double,

"sliceBy":[{

levels=["string-1" <, "string-2", ...>],

term={effect}

}<, {...}>],

"term":{effect}

}<, {...}>]

}<, {...}>],

spline=[{

"basis":"BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

"dataBoundary":True | False,

"degree":integer,

"details":True | False,

"knotMax":double,

"knotMethod":{

"equal":integer

"list":[double-1 <, double-2, ...>]

"listWithBoundary":[double-1 <, double-2, ...>]

"multiscale":{

"endScale":integer

"startScale":integer

}

"rangeFractions":[double-1 <, double-2, ...>]

"knotMin":double,

"name":"string",

"naturalCubic":True | False,

"separate":True | False,

"split":True | False,

"vars":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>],

store={

"caslib":"string",

"label":"string",

"lifetime":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"promote":True | False,

"replace":True | False,

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

table={

"caslib":"string",

"computedOnDemand":True | False,

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"computedVarsProgram":"string",

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>},

"groupBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"groupByMode":"NOSORT" | "REDISTRIBUTE",

"name":"table-name",

"orderBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

test=[{

"statements":[{

"chiSq":True | False,

"df":double,

"eType":[integer-1 <, integer-2, ...>],

"hType":[integer-1 <, integer-2, ...>],

"terms":[{effect-1} <, {effect-2}, ...>]

}<, {...}>]

}<, {...}>],

timing=True | False,

weight="variable-name"

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
lsmestimate	required parameterstatements (and nested parameter obsMarginsData)	specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.
margins	required parameterstatements (and nested parameter obsMarginsData)	specifies the effects and related parameters for predictive margins of fixed effects.
parms	parmsData	specifies the initial covariance values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
blup	required parameteroutData	creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	Stores linear mixed models to a blob (binary large object).

Parameter Descriptions

blup={mixedBlupStmt}

creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.

The mixedBlupStmt value can be one or more of the following:

"maxIter":double

specifies the maximum number of iterations.

Minimum value	0

* "outData":{casouttable}

names the table on the server that contains BLUE and BLUP values.

For more information about specifying the outData parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

"solver":"DIRECT" | "IOC" | "IOD"

names the solver that is used to obtain the BLUE and BLUP.

Default	IOC

DIRECT

requires storing mixed model equations (MMEq) in memory and computing the Cholesky decomposition of MMEq.

IOC

requires storing mixed model equations (MMEq) in memory and iterates on MMEq to solve for the solutions.

IOD

does not build mixed model equations; instead it iterates on data to solve for the solutions.

"tol":double

specifies the convergence criteria for solving the BLUP by the iteration method.

Alias	tolerance
Minimum value	0

byLimit=64-bit-integer

specifies that the analysis will not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

byOp=integer

specifies the method to use for BY-group processing.

Default	0
Range	0–1

class_=[{classStatement-1} <, {classStatement-2}, ...>]

names the classification variables to be used as explanatory variables in the analysis.

Aliases	classVars
Aliases	nominal

The classStatement value can be one or more of the following:

"countMissing":True | False

when set to True, treats missing as a valid level for this variable.

Default	False

"descending":True | False

when set to True, reverses the sort order that is imposed by the order parameter.

Default	False

"ignoreMissing":True | False

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	False

"levelizeRaw":True | False

when set to True, bases levelization for this variable on raw values.

Default	False

"maxLev":integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

"ref":"FIRST" | "LAST" | double | "string"

* "vars":["variable-name-1" <, "variable-name-2", ...>]

specifies the classification variables.

Alias	name

classglobalopts={classopts}

lists options that apply to all classification variables.

Long form	classglobalopts={"param":"EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"}
Shortcut form	classglobalopts="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

The classopts value can be one or more of the following:

"countMissing":True | False

when set to True, treats missing as a valid level for this variable.

Default	False

"descending":True | False

when set to True, reverses the sort order that is imposed by the order parameter.

Default	False

"ignoreMissing":True | False

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	False

"levelizeRaw":True | False

when set to True, bases levelization for this variable on raw values.

Default	False

"maxLev":integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

For more information, see the description of the order subparameter in the class parameter (Shared Concepts).

"param":"EFFECT" | "GLM" | "ORDINAL" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

For more information, see the description of the param subparameter in the class parameter (Shared Concepts).

"ref":"FIRST" | "LAST" | double | "string"

classLevelsPrint=True | False

when set to False, suppresses the display of class levels.

Default	True

collection=[{collection-1} <, {collection-2}, ...>]

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

The collection value can be one or more of the following:

"details":True | False

when set to True, requests a table that shows additional details that are related to this effect.

Default	False

* "name":"string"

specifies the name of the effect.

* "vars":["variable-name-1" <, "variable-name-2", ...>]

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod}

specifies the design matrix method.

The mixedDmMethod value can be one or more of the following:

* method="DENSE" | "NOSPEC" | "SPARSE"

smmethod="BASIC" | "NDORDERING"

estimate=[{estimateStmt-1} <, {estimateStmt-2}, ...>]

specifies the effects, their coefficients, and the options for a customized linear estimation.

The estimateStmt value can be one or more of the following:

"adjSimACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

"adjSimCV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"adjSimEPS":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

"adjSimNSamp":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

"adjSimReport":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"adjSimSeed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

"adjust":"BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

"ACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"CV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"epsilon":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* "method":"SIMULATE"

"nSample":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

"report":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"seed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

"alpha":double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

"chiSq":True | False

requests a chi-square test in addition to the F test.

Default	False

"cl":True | False

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	False

"df":double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

"divisor":[double-1 <, double-2, ...>]

specifies a list of values to divide the coefficients.

"e":True | False

when set to True, displays the matrix coefficients for all effects.

Default	False

"group":[{coeffEntry-1} <, {coeffEntry-2}, ...>]

The coeffEntry value can be one or more of the following:

"levelIndicator":[double-1 <, double-2, ...>]

* "LMatrixValue":double

"joint":True | False

requests a joint test for the LS-Means.

Default	False

"lower":True | False

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	False

"singular":double

tunes the estimability checking.

Default	0.0001
Range	0–1

* "statements":[{estimateList-1} <, {estimateList-2}, ...>]

The estimateList value can be one or more of the following:

"adjSimACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

"adjSimCV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"adjSimEPS":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

"adjSimNSamp":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

"adjSimReport":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"adjSimSeed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

"adjust":"BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

"ACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"CV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"epsilon":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* "method":"SIMULATE"

"nSample":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

"report":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"seed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

"alpha":double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

"chiSq":True | False

requests a chi-square test in addition to the F test.

Default	False

"cl":True | False

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	False

"df":double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

"divisor":[double-1 <, double-2, ...>]

specifies a list of values to divide the coefficients.

"e":True | False

when set to True, displays the matrix coefficients for all effects.

Default	False

* "effectCoeff":[{coeffDefinition-1} <, {coeffDefinition-2}, ...>]

specifies an effect and its non-positional coefficients.

The coeffDefinition value can be one or more of the following:

"coefficients":[{coeffEntry-1} <, {coeffEntry-2}, ...>]

The coeffEntry value can be one or more of the following:

"levelIndicator":[double-1 <, double-2, ...>]

* "LMatrixValue":double

* "terms":{effect}

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"group":[{coeffEntry-1} <, {coeffEntry-2}, ...>]

The coeffEntry value can be one or more of the following:

"levelIndicator":[double-1 <, double-2, ...>]

* "LMatrixValue":double

* "label":"string"

specifies a name for every row of the multirow estimate.

Alias	name

"lower":True | False

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	False

"singular":double

tunes the estimability checking.

Default	0.0001
Range	0–1

"subject":[{coeffEntry-1} <, {coeffEntry-2}, ...>]

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

"levelIndicator":[double-1 <, double-2, ...>]

* "LMatrixValue":double

"upper":True | False

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	False

"subject":[{coeffEntry-1} <, {coeffEntry-2}, ...>]

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

"levelIndicator":[double-1 <, double-2, ...>]

* "LMatrixValue":double

"upper":True | False

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	False

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

itDetails=True | False

when set to True, adds the covariance values to the iteration history at each step of the optimization.

Default	False

lsmeans=[{lsmeansStatement-1} <, {lsmeansStatement-2}, ...>]

specifies the effects and related parameters for least squares means of fixed effects.

* "statements":[{lsmeansList-1} <, {lsmeansList-2}, ...>]

The lsmeansList value can be one or more of the following:

"adjSimACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"adjSimCV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"adjSimEPS":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01
Range	0–1

"adjSimNSamp":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604
Minimum value	0

"adjSimReport":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"adjSimSeed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

"adjust":"BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY" | {airMCAdjustTUKEY} | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSMM} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustDUNNETT} | {airMCAdjustNELSON} | {airMCAdjustT} | {airMCAdjustNONE}

determines the adjustment method for multiple comparisons of LS-Means differences.

The airMCAdjustTUKEY value is specified as follows:

* method="TUKEY"

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSMM value is specified as follows:

* method="GT2" | "SMM"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

"ACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"CV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"epsilon":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* "method":"SIMULATE"

"nSample":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

"report":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"seed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustDUNNETT value is specified as follows:

* method="DUNNETT"

The airMCAdjustNELSON value is specified as follows:

* method="NELSON"

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

"alpha":double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

"at":"MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* "vals":double | [double-1 <, double-2, ...>]

sets values of covariates.

* "vars":"string" | ["string-1" <, "string-2", ...>]

sets names of covariates.

"cl":True | False

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	False

"controlLevel":["string-1" <, "string-2", ...>]

displays the differences with a control level of the specified least squares means effects.

"corr":True | False

when set to True, displays the estimated correlation matrix of the least squares means.

Default	False

"cov":True | False

when set to True, displays the estimated covariance matrix of the least squares means.

Default	False

"df":double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

"diff":"ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

"e":True | False

when set to True, displays the matrix coefficients for all effects.

Default	False

"singular":double

tunes the estimability checking.

Default	0.0001
Range	0–1

"slice":[{effect-1} <, {effect-2}, ...>]

specifies effects by which to partition interaction LSMEANS effects.

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* "terms":[{effect-1} <, {effect-2}, ...>] | ["string-1" <, "string-2", ...>]

specifies effects in the model for the estimates of the least squares means.

The effect value is specified as follows:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

lsmestimate=[{lsmestimateStatement-1} <, {lsmestimateStatement-2}, ...>]

specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.

* "statements":[{lsmestimateList-1} <, {lsmestimateList-2}, ...>]

The lsmestimateList value can be one or more of the following:

"adjust":{"method":"BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

"alpha":double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

"at":"MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* "vals":double | [double-1 <, double-2, ...>]

sets values of covariates.

* "vars":"string" | ["string-1" <, "string-2", ...>]

sets names of covariates.

"byLevel":True | False

computes separate margins.

Default	False

"chiSq":True | False

requests a chi-square test in addition to the F test.

Default	False

"coeff":[{namedCoeffDef-1} <, {namedCoeffDef-2}, ...>]

The namedCoeffDef value can be one or more of the following:

"coefficients":[{coeffEntry-1} <, {coeffEntry-2}, ...>]

The coeffEntry value can be one or more of the following:

"levelIndicator":[double-1 <, double-2, ...>]

* "LMatrixValue":double

"divisor":double

specifies a list of values to divide the coefficients.

* "name":"string"

specifies a name for every row of the multirow estimate.

"confLimits":True | False

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	False

"df":double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

"divisor":[double-1 <, double-2, ...>]

specifies a list of values to divide the coefficients.

"joint":True | False

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Default	False

"lower":True | False

performs one-sided, lower-tailed inference.

Alias	lowerTailed
Default	False

"obsMargins":True | False

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	False

"obsMarginsData":{castable}

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

"caslib":"string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

"computedOnDemand":True | False

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	False

"computedVars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

Alias	compVars

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"computedVarsProgram":"string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

"groupBy":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"groupByMode":"NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the input table.

"orderBy":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"singlePass":True | False

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	False

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the input data.

"whereTable":{groupbytable}

The groupbytable value can be one or more of the following:

"casLib":"string"

specifies the caslib for the filter table. By default, the active caslib is used.

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the filter table.

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the data from the filter table.

"printCoef":True | False

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	False

"printCorr":True | False

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	False

"printCov":True | False

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	False

"printKCoef":True | False

when set to True, displays the K matrix coefficients for the specified effects.

Alias	elsm
Default	False

"singular":double

tunes the estimability checking.

Default	0.0001
Range	0–1

* "terms":[{effect-1} <, {effect-2}, ...>]

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"upper":True | False

performs one-sided, upper-tailed inference.

Alias	upperTailed
Default	False

margins=[{marginsStatement-1} <, {marginsStatement-2}, ...>]

specifies the effects and related parameters for predictive margins of fixed effects.

* "statements":[{marginsList-1} <, {marginsList-2}, ...>]

The marginsList value can be one or more of the following:

"adjust":{"method":"BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

"alpha":double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

"at":"MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* "vals":double | [double-1 <, double-2, ...>]

sets values of covariates.

* "vars":"string" | ["string-1" <, "string-2", ...>]

sets names of covariates.

"byLevel":True | False

computes separate margins.

Default	False

"chiSq":True | False

requests a chi-square test in addition to the F test.

Default	False

"confLimits":True | False

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	False

"controlLevel":["string-1" <, "string-2", ...>]

displays the differences with a control level of the specified least squares means effects.

"df":double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

"diff":"ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

"joint":True | False

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Alias	fTest
Default	False

"lines":True | False

produces 'Lines' display for pairwise LS-Means difference.

Default	False

"obsMargins":True | False

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	False

"obsMarginsData":{castable}

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

"caslib":"string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

"computedOnDemand":True | False

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	False

"computedVars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

Alias	compVars

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"computedVarsProgram":"string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies data source options.

Aliases	options
Aliases	dataSource

"groupBy":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"groupByMode":"NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the input table.

"orderBy":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"singlePass":True | False

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	False

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the input data.

"whereTable":{groupbytable}

The groupbytable value can be one or more of the following:

"casLib":"string"

specifies the caslib for the filter table. By default, the active caslib is used.

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias	import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the filter table.

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the data from the filter table.

"odds":True | False

reports odds of levels of fixed effects if permissible by the link function.

Default	False

"oddsRatio":True | False

reports differences of LS-Means in terms of odds ratios by the link function.

Default	False

"printCoef":True | False

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	False

"singular":double

tunes the estimability checking.

Default	0.0001
Range	0–1

"slice":[{effect-1} <, {effect-2}, ...>]

specifies effects by which to partition interaction LSMEANS effects.

Alias	sliceBy

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"sliceControlLevel":["string-1" <, "string-2", ...>]

requests slice effects differences with a control level of each of the specified LSMEANS effects.

"sliceDiff":"ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

determines the type of simple effects differences.

Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

* "terms":[{effect-1} <, {effect-2}, ...>]

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"weight":True | False

when set to True, computes the weighted predictive margins.

Default	False

maxClPrint=integer

specifies the maximum levels of classification variables to print in the ClassLevels table.

Default	20

method="MIVQUE0" | "ML" | "REML"

specifies the estimation method for covariance estimation analysis.

Default	REML

MIVQUE0

performs minimum variance quadratic unbiased estimation (MIVQUE0).

ML

performs maximum likelihood estimation (ML).

REML

performs residual (restricted) maximum likelihood estimation (REML).

mmeq=True | False

when set to True, displays the mixed model equations table.

Default	False

* model={mixedModelStmt}

names the dependent variable, explanatory effects, and model options.

The mixedModelStmt value can be one or more of the following:

"alpha":double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

"cl":True | False

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	False

"ddf":[double-1 <, double-2, ...>]

specifies a list of the customized denominator degrees of freedom for the fixed effects.

"depVars":[{responsevar-1} <, {responsevar-2}, ...>]

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

"name":"variable-name"

names the response variable.

"options":{modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

"descending":True | False

Default	False

"event":"FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

"levelType":"BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

"ref":"FIRST" | "LAST" | double | "string"

"dfMethod":"CONTAIN" | "NONE" | "RESIDUAL"

specifies the degrees of freedom method.

Alias	ddfm
Default	RESIDUAL

"dist":"BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL"

specifies the response distribution for the model.

"effects":[{effect-1} <, {effect-2}, ...>]

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"link":"CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO"

specifies the link function for the model.

"noint":True | False

when set to True, does not include the intercept term in the model.

Default	False

"offset":"variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

"printDenseSol":True | False

when set to True, displays the fixed effects estimates.

Default	False

"printSol":True | False

when set to True, displays the fixed effects estimates.

Default	False

"trial":"variable-name"

"zeta":double

specifies a value to tune the estimability check.

Range	0–1

multimember=[{multimember-1} <, {multimember-2}, ...>]

For more information about specifying the multimember parameter, see the common multimember parameter (Appendix A: Common Parameters).

nClassLevelsPrint=integer

limits the display of class levels. The value 0 suppresses all levels.

Minimum value	0

noBlupVar=True | False

when set to True, suppresses computing the covariances of random-effects estimates that are used in output statistics.

Default	False

noBound=True | False

when set to True, enforces no boundary restriction for estimating covariance parameters.

Default	False

noClPrint=integer

Default	0

noInfo=True | False

when set to True, suppresses the display of the Model Information, Number of Observations, and Dimensions tables.

Default	False

noItPrint=True | False

when set to True, suppresses the display of the Iteration History table.

Default	False

noPrint=True | False

when set to True, suppresses the display of results.

Default	False

noProfile=True | False

when set to True, includes the residual variance as one of the covariance values in the optimization iterations.

Default	False

optimization={mixedOptimizationStmt}

specifies the technique and options for performing the optimization.

Long form	optimization={"technique":"ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"}
Shortcut form	optimization="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"

The mixedOptimizationStmt value can be one or more of the following:

"absConv":double

specifies the absolute function convergence criterion.

Minimum value	0

"absConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"absFConv":double

specifies the absolute function difference convergence criterion.

Minimum value	0

"absFConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"absGConv":double

specifies the absolute gradient convergence criterion.

Minimum value	0

"absGConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"absXConv":double

specifies the absolute convergence criterion.

Minimum value	0

"absXConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"fConv":double

specifies the relative function difference convergence criterion.

Minimum value	0

"fConv2":double

specifies the second relative function difference convergence criterion.

Minimum value	0

"fConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"fSize":double

specifies the value to use for both the relative function convergence criterion and the relative gradient convergence criterion.

Minimum value	0

"gConv":double

specifies the relative gradient convergence criterion.

Minimum value	0

"gConv2":double

specifies the second relative gradient convergence criterion.

Minimum value	0

"gConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"maxFunc":double

specifies the maximum number of function evaluations.

Minimum value	0

"maxIter":double

specifies the maximum number of iterations.

Default	200
Minimum value	0

"maxTime":double

specifies the maximum allowed computing time in seconds.

Minimum value	0

"minIter":integer

specifies the minimum number of iterations.

Minimum value	0

"optTol":double

defines the measure by which you can decide whether the value in the current iteration is an acceptable approximation of a local minimum.

Default	1E-05
Minimum value	0

"technique":"ACTIVESET" | "CONGRA" | "DBLDOG" | "IPDIRECT" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "QUANEW" | "TRUREG"

specifies the optimization technique.

ACTIVESET

uses the active set method.

CONGRA

uses the conjugate gradient method, which requires first-order derivatives.

DBLDOG

uses the double-dogleg method, which requires first-order derivatives.

IPDIRECT

uses the direct interior point method.

LBFGS

uses the Limited-memory BFGS solver, which requires first-order derivatives.

NEWRAP

uses the Newton-Raphson method with line search and ridging, which requires first- and second-order derivatives.

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed using the initial covariance values.

NRRIDG

uses the Newton-Raphson method with ridging, which requires first- and second-order derivatives.

QUANEW

uses the dual quasi-Newton method, which requires first-order derivatives.

TRUREG

uses the trust region method, which requires first- and second-order derivatives.

"xConv":double

specifies the relative convergence criterion.

Minimum value	0

"xConvNum":integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

"xSize":double

specifies the value to use for the relative convergence criterion.

Minimum value	0

output={mixedOutputStmt}

creates a table on the server that contains observationwise statistics, which are computed after fitting the model.

The mixedOutputStmt value can be one or more of the following:

"allStats":True | False

when set to True, requests all available statistics.

Default	False

"alpha":double

specifies the significance level to use in output statistics. The default value is 0.05.

Default	0.05
Range	0–1

* "casOut":{casouttable}

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

"copyVars":"ALL" | "ALL_MODEL" | "ALL_NUMERIC" | ["variable-name-1" <, "variable-name-2", ...>]

"lcl":"string"

names the lower bound of a confidence interval for the linear predictor.

"lclPA":"string"

names the lower bound of a confidence interval for the marginal linear predictor.

"noMiss":True | False

when set to True, writes only observations that are used in the analysis to the output data table. By default, output statistics for all observations are written to the output data table.

Default	False

"pearson":"string"

names the Pearson-type residual.

"pearsonPA":"string"

names the marginal Pearson-type residual.

"pred":"string"

names the linear predictor. If no output statistics are specified, then this is the default.

Aliases	p
Aliases	predicted

"predPA":"string"

names the marginal linear predictor.

"resid":"string"

names the residual, which is calculated as ACTUAL minus PREDICTED.

Aliases	r
Aliases	residual

"residPA":"string"

names the marginal standard deviation of the linear predictor.

"stderr":"string"

names the standard deviation of the linear predictor.

"stderrPA":"string"

names the marginal standard deviation of the linear predictor.

"student":"string"

names the studentized residuals, which are the residuals divided by their standard errors.

"studentPA":"string"

names the marginal residual.

"ucl":"string"

names the upper bound of a confidence interval for the linear predictor.

"uclPA":"string"

names the upper bound of a confidence interval for the marginal linear predictor.

"variance":"string"

names the conditional variance of the response variable.

"variancePA":"string"

names the marginal variance of the response variable.

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	displayOut

parms={mixedParmsStmt}

specifies the initial covariance values.

The mixedParmsStmt value can be one or more of the following:

"hold":[integer-1 <, integer-2, ...>]

holds all or partial covariance values.

Alias	eqcons

"holdAll":True | False

when set to True, holds all covariance values.

Default	False

"initvals":[[list-1] <,[list-2], ...>]

specifies the initial covariance values.

"lowerB":[double-1 <, double-2, ...>]

specifies the lower boundary for covariance values.

"noIter":True | False

when set to True, performs no iteration for estimating covariance parameters.

Default	False

"parmsData":{castable}

names the data table that contains the initial covariance values.

For more information about specifying the parmsData parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

Alias	pData

"residVar":double

specifies a value for residual variance and excludes it from optimization search.

Minimum value	1E-08

"upperB":[double-1 <, double-2, ...>]

specifies the upper boundary for covariance values.

polynomial=[{polynomial-1} <, {polynomial-2}, ...>]

For more information about specifying the polynomial parameter, see the common polynomial parameter (Appendix A: Common Parameters).

Alias	poly

random=[{mixedRandomStmt-1} <, {mixedRandomStmt-2}, ...>]

specifies random effects and related options. Notice that no-intercept is inherited from the model specification where default value is FALSE, but the default value in random specification is TRUE.

The mixedRandomStmt value can be one or more of the following:

"alpha":double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

"cl":True | False

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	False

"covType":"ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC"

specifies the type of covariance structure.

Default	VC

ANTE

specifies the first-order antedependence covariance structure.

AR

specifies the first-order autoregressive covariance structure.

ARH

specifies the heterogeneous first-order autoregressive covariance structure.

ARMA

specifies the autoregressive moving average covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

FA

specifies the factor-analytic covariance structure.

FA0

specifies the no-diagonal factor-analytic covariance structure.

FA1

specifies the equal-diagonal factor-analytic covariance structure.

HF

specifies the Huynh-Feldt covariance structure.

TOEP

specifies the Toeplitz covariance structure.

TOEPH

specifies the heterogeneous Toeplitz covariance structure.

UC

specifies the uniform correlation covariance structure.

UN

specifies the unstructured covariance.

UNR

specifies the unstructured correlation covariance structure.

VC

specifies the variance components structure.

"depVars":[{responsevar-1} <, {responsevar-2}, ...>]

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

"name":"variable-name"

names the response variable.

"options":{modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

"descending":True | False

Default	False

"event":"FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

"levelType":"BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

"ref":"FIRST" | "LAST" | double | "string"

"effects":[{effect-1} <, {effect-2}, ...>]

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"group":[{effect-1} <, {effect-2}, ...>]

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"noint":True | False

when set to True, does not include the intercept term in the model.

Default	False

"order":integer

specifies the order of covariance structure.

"printSol":True | False

when set to True, displays the random-effects estimates. You can set this parameter to True by specifying the alpha or cl parameters in the model specification.

Default	False

"subject":[{effect-1} <, {effect-2}, ...>]

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"trial":"variable-name"

ranks=True | False

when set to True, displays the table of ranks of the matrices X, (XZ), and MMEq.

Default	False

repeated=[{mixedRepeatedStmt-1} <, {mixedRepeatedStmt-2}, ...>]

The mixedRepeatedStmt value can be one or more of the following:

"covType":"AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC"

specifies the type of covariance structure.

Default	VC

AR

specifies the first-order autoregressive covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

UN

specifies the unstructured covariance.

VC

specifies the variance components structure.

"depVars":[{responsevar-1} <, {responsevar-2}, ...>]

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

"name":"variable-name"

names the response variable.

"options":{modelopts}

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

"descending":True | False

Default	False

"event":"FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

"levelType":"BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

"ref":"FIRST" | "LAST" | double | "string"

"effects":[{effect-1} <, {effect-2}, ...>]

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"group":[{effect-1} <, {effect-2}, ...>]

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"noConvert":True | False

when set to True, do not convert the repeated model to a simple one.

Default	False

"noint":True | False

when set to True, does not include the intercept term in the model.

Default	False

"order":integer

specifies the order of covariance structure.

"printR":[integer-1 <, integer-2, ...>]

displays the blocks of the estimated R matrix.

"printRC":[integer-1 <, integer-2, ...>]

displays the Cholesky root of the estimated R matrix.

"printRCI":[integer-1 <, integer-2, ...>]

displays the inverse of the Cholesky root of the estimated R matrix.

"printRCorr":[integer-1 <, integer-2, ...>]

displays the correlation matrix that corresponds to the estimated R matrix.

"printRI":[integer-1 <, integer-2, ...>]

displays the inverse of the blocks of the estimated R matrix.

"subject":[{effect-1} <, {effect-2}, ...>]

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

"trial":"variable-name"

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

simpleStat=True | False

when set to True, displays the Descriptive Statistics table.

Default	False

singChol=double

tunes the singularity criterion for Cholesky decompositions.

Range	0–1

singRes=double

tunes the singularity criterion for the residual variance.

Range	0–1

singular=double

tunes the general singularity criterion.

Range	0–1

slice=[{sliceStatement-1} <, {sliceStatement-2}, ...>]

specifies the effects and related parameters for a partitioned analysis of the LS-Means for an interaction.

* "statements":[{sliceList-1} <, {sliceList-2}, ...>]

The sliceList value can be one or more of the following:

"adjust":{"method":"BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters}

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

"alpha":double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

"at":"MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* "vals":double | [double-1 <, double-2, ...>]

sets values of covariates.

* "vars":"string" | ["string-1" <, "string-2", ...>]

sets names of covariates.

"confLimits":True | False

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	False

"controlLevel":["string-1" <, "string-2", ...>]

displays the differences with a control level of the specified least squares means effects.

"df":double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

"diff":"ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

"fTest":True | False

when set to True, requests the F test for testing the mutual equality of the estimable functions in the partitioned analysis of LS-Means.

Default	True

"lines":True | False

produces 'Lines' display for pairwise LS-Means difference.

Default	False

"means":True | False

specifies to use the covariates means in the partitioned analysis of LS-Means.

Default	False

"oddsRatio":True | False

reports differences of LS-Means in terms of odds ratios by the link function.

Default	False

"printCoef":True | False

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	False

"printCorr":True | False

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	False

"printCov":True | False

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	False

"singular":double

tunes the estimability checking.

Default	0.0001
Range	0–1

"sliceBy":[{slicebyDef-1} <, {slicebyDef-2}, ...>]

specifies effects by which to partition interaction LSMEANS effects.

The slicebyDef value can be one or more of the following:

"levels":["string-1" <, "string-2", ...>]

* "term":{effect}

The effect value can be one or more of the following:

"interaction":"BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* "term":{effect}

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

spline=[{spline-1} <, {spline-2}, ...>]

expands variables into spline bases whose form depends on the specified parameters.

For more information about specifying the spline parameter, see the common spline parameter (Appendix A: Common Parameters).

store={casouttable}

Stores linear mixed models to a blob (binary large object).

Alias	saveState

Long form	store={"name":"table-name"}
Shortcut form	store="table-name"

The casouttable value can be one or more of the following:

"caslib":"string"

specifies the name of the caslib for the output table.

"label":"string"

specifies the descriptive label to associate with the table.

"lifetime":64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

STANDARD

use the standard memory format.

"name":"table-name"

specifies the name for the output table.

"promote":True | False

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	False

"replace":True | False

when set to True, overwrites an existing table that has the same name.

Default	False

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

test=[{testStatement-1} <, {testStatement-2}, ...>]

specifies the effects and related parameters for hypothesis test of fixed effects.

* "statements":[{testList-1} <, {testList-2}, ...>]

The testList value can be one or more of the following:

"chiSq":True | False

requests a chi-square test in addition to the F test.

Default	False

"df":double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

"eType":[integer-1 <, integer-2, ...>]

specifies the type of coefficients to display.

"hType":[integer-1 <, integer-2, ...>]

specifies the type of hypothesis test to perform on the specified effects.

Default	3

* "terms":[{effect-1} <, {effect-2}, ...>]

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

"interaction":"CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"nest":["string-1" <, "string-2", ...>]

* "vars":["string-1" <, "string-2", ...>]

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

timing=True | False

when set to True, displays the Timing table.

Default	False

weight="variable-name"

names the numeric variable to use in performing a weighted analysis of the data.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

"ACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"CV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"epsilon":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

"nSample":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

"report":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"seed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

"ACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"CV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"epsilon":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

"nSample":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

"report":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"seed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NELSON"

No parameters apply when you specify NELSON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

"ACC":double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

"CV":True | False

specifies CV option in ADJUST=SIMULATE.

Default	False

"epsilon":double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

"nSample":64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

"report":True | False

specifies REPORT option in ADJUST=SIMULATE.

Default	False

"seed":64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

mixed Action

Fits linear mixed models.

R Syntax
Summary: Input and Output Tables
Parameter Descriptions

R Syntax

results <– cas.mixed.mixed(s,

blup=list(

maxIter=double,

outData=list(

caslib="string"

compress=TRUE | FALSE

indexVars=list("variable-name-1" <, "variable-name-2", ...>)

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

solver="DIRECT" | "IOC" | "IOD",

tol=double

byLimit=64-bit-integer,

byOp=integer,

class=list( list(

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string",

vars=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>),

classglobalopts=list(

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string"

classLevelsPrint=TRUE | FALSE,

collection=list( list(

details=TRUE | FALSE,

name="string",

vars=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>),

display=list(

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

names=list("string-1" <, "string-2", ...>),

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | list(mixedDmMethod),

estimate=list( list(

adjSimACC=double,

adjSimCV=TRUE | FALSE,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=TRUE | FALSE,

adjSimSeed=64-bit-integer,

alpha=double,

chiSq=TRUE | FALSE,

cl=TRUE | FALSE,

df=double,

divisor=list(double-1 <, double-2, ...>),

e=TRUE | FALSE,

group=list( list(

levelIndicator=list(double-1 <, double-2, ...>),

LMatrixValue=double

) <, list(...)>),

joint=TRUE | FALSE,

lower=TRUE | FALSE,

singular=double,

statements=list( list(

adjSimACC=double,

adjSimCV=TRUE | FALSE,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=TRUE | FALSE,

adjSimSeed=64-bit-integer,

alpha=double,

chiSq=TRUE | FALSE,

cl=TRUE | FALSE,

df=double,

divisor=list(double-1 <, double-2, ...>),

e=TRUE | FALSE,

effectCoeff=

coefficients=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>),

terms=list(effect)

group=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>),

label="string",

lower=TRUE | FALSE,

singular=double,

subject=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>),

upper=TRUE | FALSE

) <, list(...)>),

subject=list( list(

levelIndicator=list(double-1 <, double-2, ...>),

LMatrixValue=double

) <, list(...)>),

upper=TRUE | FALSE

) <, list(...)>),

freq="variable-name",

itDetails=TRUE | FALSE,

lsmeans=list( list(

statements=list( list(

adjSimACC=double,

adjSimCV=TRUE | FALSE,

adjSimEPS=double,

adjSimNSamp=64-bit-integer,

adjSimReport=TRUE | FALSE,

adjSimSeed=64-bit-integer,

alpha=double,

at="MEANS" | list(lsmeansOptionAt),

cl=TRUE | FALSE,

controlLevel=list("string-1" <, "string-2", ...>),

corr=TRUE | FALSE,

cov=TRUE | FALSE,

df=double,

e=TRUE | FALSE,

singular=double,

slice=list( list(effect-1) <, list(effect-2), ...>),

terms=list( list(effect-1) <, list(effect-2), ...>) | list("string-1" <, "string-2", ...>)

) <, list(...)>)

) <, list(...)>),

lsmestimate=list( list(

statements=list( list(

alpha=double,

at="MEANS" | list(lsmeansOptionAt),

byLevel=TRUE | FALSE,

chiSq=TRUE | FALSE,

coeff=list( list(namedCoeffDef-1) <, list(namedCoeffDef-2), ...>),

confLimits=TRUE | FALSE,

df=double,

divisor=list(double-1 <, double-2, ...>),

joint=TRUE | FALSE,

lower=TRUE | FALSE,

obsMargins=TRUE | FALSE,

obsMarginsData=list(castable),

printCoef=TRUE | FALSE,

printCorr=TRUE | FALSE,

printCov=TRUE | FALSE,

printKCoef=TRUE | FALSE,

singular=double,

terms=list( list(effect-1) <, list(effect-2), ...>),

upper=TRUE | FALSE

) <, list(...)>)

) <, list(...)>),

margins=list( list(

statements=list( list(

alpha=double,

at="MEANS" | list(lsmeansOptionAt),

byLevel=TRUE | FALSE,

chiSq=TRUE | FALSE,

confLimits=TRUE | FALSE,

controlLevel=list("string-1" <, "string-2", ...>),

df=double,

joint=TRUE | FALSE,

lines=TRUE | FALSE,

obsMargins=TRUE | FALSE,

obsMarginsData=list(castable),

odds=TRUE | FALSE,

oddsRatio=TRUE | FALSE,

printCoef=TRUE | FALSE,

singular=double,

slice=list( list(effect-1) <, list(effect-2), ...>),

sliceControlLevel=list("string-1" <, "string-2", ...>),

terms=list( list(effect-1) <, list(effect-2), ...>),

weight=TRUE | FALSE

) <, list(...)>)

) <, list(...)>),

maxClPrint=integer,

method="MIVQUE0" | "ML" | "REML",

mmeq=TRUE | FALSE,

model=list(

alpha=double,

cl=TRUE | FALSE,

ddf=list(double-1 <, double-2, ...>),

depVars=list( list(

name="variable-name",

options=list(modelopts)

) <, list(...)>),

dfMethod="CONTAIN" | "NONE" | "RESIDUAL",

effects=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

noint=TRUE | FALSE,

offset="variable-name",

printDenseSol=TRUE | FALSE,

printSol=TRUE | FALSE,

trial="variable-name",

zeta=double

multimember=list( list(

details=TRUE | FALSE,

name="string",

noEffect=TRUE | FALSE,

stdize=TRUE | FALSE,

vars=list("variable-name-1" <, "variable-name-2", ...>),

weight=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>),

nClassLevelsPrint=integer,

noBlupVar=TRUE | FALSE,

noBound=TRUE | FALSE,

noClPrint=integer,

noInfo=TRUE | FALSE,

noItPrint=TRUE | FALSE,

noPrint=TRUE | FALSE,

noProfile=TRUE | FALSE,

optimization=list(

absConv=double,

absConvNum=integer,

absFConv=double,

absFConvNum=integer,

absGConv=double,

absGConvNum=integer,

absXConv=double,

absXConvNum=integer,

fConv=double,

fConv2=double,

fConvNum=integer,

fSize=double,

gConv=double,

gConv2=double,

gConvNum=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

optTol=double,

xConv=double,

xConvNum=integer,

xSize=double

output=list(

allStats=TRUE | FALSE,

alpha=double,

casOut=list(

caslib="string"

compress=TRUE | FALSE

indexVars=list("variable-name-1" <, "variable-name-2", ...>)

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("variable-name-1" <, "variable-name-2", ...>),

lcl="string",

lclPA="string",

noMiss=TRUE | FALSE,

pearson="string",

pearsonPA="string",

pred="string",

predPA="string",

resid="string",

residPA="string",

stderr="string",

stderrPA="string",

student="string",

studentPA="string",

ucl="string",

uclPA="string",

variance="string",

variancePA="string"

outputTables=list(

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

names=list("string-1" <, "string-2", ...>) | list(key-1=list(casouttable-1) <, key-2=list(casouttable-2), ...>),

repeated=TRUE | FALSE,

replace=TRUE | FALSE

parms=list(

hold=list(integer-1 <, integer-2, ...>),

holdAll=TRUE | FALSE,

initvals=list( list(list-1) <, list(list-2), ...>),

lowerB=list(double-1 <, double-2, ...>),

noIter=TRUE | FALSE,

parmsData=list(

caslib="string"

computedOnDemand=TRUE | FALSE

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

computedVarsProgram="string"

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

groupBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

groupByMode="NOSORT" | "REDISTRIBUTE"

name="table-name"

orderBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

singlePass=TRUE | FALSE

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

residVar=double,

upperB=list(double-1 <, double-2, ...>)

polynomial=list( list(

degree=integer,

details=TRUE | FALSE,

labelStyle=list(

expand=TRUE | FALSE

exponent="string"

includeName=TRUE | FALSE

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=TRUE | FALSE,

standardize=list(

method="MOMENTS" | "MRANGE" | "WMOMENTS"

options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

prefix="NONE" | "string"

vars=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>),

random=list( list(

alpha=double,

cl=TRUE | FALSE,

covType="ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC",

depVars=list( list(

name="variable-name",

options=list(modelopts)

) <, list(...)>),

effects=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

group=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

noint=TRUE | FALSE,

order=integer,

printSol=TRUE | FALSE,

subject=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

trial="variable-name"

) <, list(...)>),

ranks=TRUE | FALSE,

repeated=list( list(

covType="AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC",

depVars=list( list(

name="variable-name",

options=list(modelopts)

) <, list(...)>),

effects=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

group=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

noConvert=TRUE | FALSE,

noint=TRUE | FALSE,

order=integer,

printR=list(integer-1 <, integer-2, ...>),

printRC=list(integer-1 <, integer-2, ...>),

printRCI=list(integer-1 <, integer-2, ...>),

printRCorr=list(integer-1 <, integer-2, ...>),

printRI=list(integer-1 <, integer-2, ...>),

subject=list( list(

interaction="BAR" | "CROSS" | "NONE",

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

trial="variable-name"

) <, list(...)>),

seed=64-bit-integer,

simpleStat=TRUE | FALSE,

singChol=double,

singRes=double,

singular=double,

slice=list( list(

statements=list( list(

alpha=double,

at="MEANS" | list(lsmeansOptionAt),

confLimits=TRUE | FALSE,

controlLevel=list("string-1" <, "string-2", ...>),

df=double,

fTest=TRUE | FALSE,

lines=TRUE | FALSE,

means=TRUE | FALSE,

oddsRatio=TRUE | FALSE,

printCoef=TRUE | FALSE,

printCorr=TRUE | FALSE,

printCov=TRUE | FALSE,

singular=double,

sliceBy=

levels=list("string-1" <, "string-2", ...>),

term=list(effect)

) <, list(...)>)

) <, list(...)>),

spline=list( list(

basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

dataBoundary=TRUE | FALSE,

degree=integer,

details=TRUE | FALSE,

knotMax=double,

knotMethod=list(

equal=integer

list=list(double-1 <, double-2, ...>)

listWithBoundary=list(double-1 <, double-2, ...>)

multiscale=list(

endScale=integer

startScale=integer

)

rangeFractions=list(double-1 <, double-2, ...>)

knotMin=double,

name="string",

naturalCubic=TRUE | FALSE,

separate=TRUE | FALSE,

split=TRUE | FALSE,

vars=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>),

store=list(

caslib="string",

label="string",

lifetime=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=TRUE | FALSE,

replace=TRUE | FALSE,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

table=list(

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

computedVarsProgram="string",

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),

groupBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

orderBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

singlePass=TRUE | FALSE,

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

where="where-expression",

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

test=list( list(

statements=list( list(

chiSq=TRUE | FALSE,

df=double,

eType=list(integer-1 <, integer-2, ...>),

hType=list(integer-1 <, integer-2, ...>),

terms=list( list(effect-1) <, list(effect-2), ...>)

) <, list(...)>)

) <, list(...)>),

timing=TRUE | FALSE,

weight="variable-name"

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
lsmestimate	required parameterstatements (and nested parameter obsMarginsData)	specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.
margins	required parameterstatements (and nested parameter obsMarginsData)	specifies the effects and related parameters for predictive margins of fixed effects.
parms	parmsData	specifies the initial covariance values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
blup	required parameteroutData	creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	Stores linear mixed models to a blob (binary large object).

Parameter Descriptions

blup=list(mixedBlupStmt)

creates a table on the server that contains best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) values.

The mixedBlupStmt value can be one or more of the following:

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

* outData=list(casouttable)

names the table on the server that contains BLUE and BLUP values.

For more information about specifying the outData parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

solver="DIRECT" | "IOC" | "IOD"

names the solver that is used to obtain the BLUE and BLUP.

Default	IOC

DIRECT

requires storing mixed model equations (MMEq) in memory and computing the Cholesky decomposition of MMEq.

IOC

requires storing mixed model equations (MMEq) in memory and iterates on MMEq to solve for the solutions.

IOD

does not build mixed model equations; instead it iterates on data to solve for the solutions.

tol=double

specifies the convergence criteria for solving the BLUP by the iteration method.

Alias	tolerance
Minimum value	0

byLimit=64-bit-integer

specifies that the analysis will not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

byOp=integer

specifies the method to use for BY-group processing.

Default	0
Range	0–1

class=list( list(classStatement-1) <, list(classStatement-2), ...>)

names the classification variables to be used as explanatory variables in the analysis.

Aliases	classVars
Aliases	nominal

The classStatement value can be one or more of the following:

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level for this variable.

Default	FALSE

descending=TRUE | FALSE

when set to True, reverses the sort order that is imposed by the order parameter.

Default	FALSE

ignoreMissing=TRUE | FALSE

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	FALSE

levelizeRaw=TRUE | FALSE

when set to True, bases levelization for this variable on raw values.

Default	FALSE

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

ref="FIRST" | "LAST" | double | "string"

* vars=list("variable-name-1" <, "variable-name-2", ...>)

specifies the classification variables.

Alias	name

classglobalopts=list(classopts)

lists options that apply to all classification variables.

Long form	classglobalopts=list(param="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE")
Shortcut form	classglobalopts="EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

The classopts value can be one or more of the following:

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level for this variable.

Default	FALSE

descending=TRUE | FALSE

when set to True, reverses the sort order that is imposed by the order parameter.

Default	FALSE

ignoreMissing=TRUE | FALSE

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	FALSE

levelizeRaw=TRUE | FALSE

when set to True, bases levelization for this variable on raw values.

Default	FALSE

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

For more information, see the description of the order subparameter in the class parameter (Shared Concepts).

param="EFFECT" | "GLM" | "ORDINAL" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

For more information, see the description of the param subparameter in the class parameter (Shared Concepts).

ref="FIRST" | "LAST" | double | "string"

classLevelsPrint=TRUE | FALSE

when set to False, suppresses the display of class levels.

Default	TRUE

collection=list( list(collection-1) <, list(collection-2), ...>)

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

The collection value can be one or more of the following:

details=TRUE | FALSE

when set to True, requests a table that shows additional details that are related to this effect.

Default	FALSE

* name="string"

specifies the name of the effect.

* vars=list("variable-name-1" <, "variable-name-2", ...>)

display=list(displayTables)

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

dmMethod="DENSE" | "NOSPEC" | "SPARSE" | {mixedDmMethod}

specifies the design matrix method.

The mixedDmMethod value can be one or more of the following:

* method="DENSE" | "NOSPEC" | "SPARSE"

smmethod="BASIC" | "NDORDERING"

estimate=list( list(estimateStmt-1) <, list(estimateStmt-2), ...>)

specifies the effects, their coefficients, and the options for a customized linear estimation.

The estimateStmt value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

adjSimCV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

adjSimReport=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

cl=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor=list(double-1 <, double-2, ...>)

specifies a list of values to divide the coefficients.

e=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Default	FALSE

group=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>)

The coeffEntry value can be one or more of the following:

levelIndicator=list(double-1 <, double-2, ...>)

* LMatrixValue=double

joint=TRUE | FALSE

requests a joint test for the LS-Means.

Default	FALSE

lower=TRUE | FALSE

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

* statements=list( list(estimateList-1) <, list(estimateList-2), ...>)

The estimateList value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005

adjSimCV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604

adjSimReport=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T" | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustT} | {airMCAdjustNONE}

specifies the method for multiple comparison adjustment of estimates.

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

determines the confidence level (1 - alpha).

Default	0.05
Range	0–1

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

cl=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor=list(double-1 <, double-2, ...>)

specifies a list of values to divide the coefficients.

e=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Default	FALSE

* effectCoeff=list( list(coeffDefinition-1) <, list(coeffDefinition-2), ...>)

specifies an effect and its non-positional coefficients.

The coeffDefinition value can be one or more of the following:

coefficients=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>)

The coeffEntry value can be one or more of the following:

levelIndicator=list(double-1 <, double-2, ...>)

* LMatrixValue=double

* terms=list(effect)

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>)

The coeffEntry value can be one or more of the following:

levelIndicator=list(double-1 <, double-2, ...>)

* LMatrixValue=double

* label="string"

specifies a name for every row of the multirow estimate.

Alias	name

lower=TRUE | FALSE

performs one-sided, lower-tailed inference.

Alias	lowertailed
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

subject=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>)

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

levelIndicator=list(double-1 <, double-2, ...>)

* LMatrixValue=double

upper=TRUE | FALSE

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	FALSE

subject=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>)

identifies the subjects in a mixed model.

The coeffEntry value can be one or more of the following:

levelIndicator=list(double-1 <, double-2, ...>)

* LMatrixValue=double

upper=TRUE | FALSE

performs one-sided, upper-tailed inference.

Alias	uppertailed
Default	FALSE

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

itDetails=TRUE | FALSE

when set to True, adds the covariance values to the iteration history at each step of the optimization.

Default	FALSE

lsmeans=list( list(lsmeansStatement-1) <, list(lsmeansStatement-2), ...>)

specifies the effects and related parameters for least squares means of fixed effects.

* statements=list( list(lsmeansList-1) <, list(lsmeansList-2), ...>)

The lsmeansList value can be one or more of the following:

adjSimACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

adjSimCV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

adjSimEPS=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Default	0.01
Range	0–1

adjSimNSamp=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Default	12604
Minimum value	0

adjSimReport=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

adjSimSeed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

adjust="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY" | {airMCAdjustTUKEY} | {airMCAdjustBON} | {airMCAdjustSIDAK} | {airMCAdjustSMM} | {airMCAdjustSCHEFFE} | {airMCAdjustSIMULATE} | {airMCAdjustDUNNETT} | {airMCAdjustNELSON} | {airMCAdjustT} | {airMCAdjustNONE}

determines the adjustment method for multiple comparisons of LS-Means differences.

The airMCAdjustTUKEY value is specified as follows:

* method="TUKEY"

The airMCAdjustBON value is specified as follows:

* method="BON"

The airMCAdjustSIDAK value is specified as follows:

* method="SIDAK"

The airMCAdjustSMM value is specified as follows:

* method="GT2" | "SMM"

The airMCAdjustSCHEFFE value is specified as follows:

* method="SCHEFFE"

The airMCAdjustSIMULATE value can be one or more of the following:

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

* method="SIMULATE"

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

The airMCAdjustDUNNETT value is specified as follows:

* method="DUNNETT"

The airMCAdjustNELSON value is specified as follows:

* method="NELSON"

The airMCAdjustT value is specified as follows:

* method="T"

The airMCAdjustNONE value is specified as follows:

* method="NONE"

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | list(double-1 <, double-2, ...>)

sets values of covariates.

* vars="string" | list("string-1" <, "string-2", ...>)

sets names of covariates.

cl=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Default	FALSE

controlLevel=list("string-1" <, "string-2", ...>)

displays the differences with a control level of the specified least squares means effects.

corr=TRUE | FALSE

when set to True, displays the estimated correlation matrix of the least squares means.

Default	FALSE

cov=TRUE | FALSE

when set to True, displays the estimated covariance matrix of the least squares means.

Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

e=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

slice=list( list(effect-1) <, list(effect-2), ...>)

specifies effects by which to partition interaction LSMEANS effects.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* terms=list( list(effect-1) <, list(effect-2), ...>) | list("string-1" <, "string-2", ...>)

specifies effects in the model for the estimates of the least squares means.

The effect value is specified as follows:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

lsmestimate=list( list(lsmestimateStatement-1) <, list(lsmestimateStatement-2), ...>)

specifies the effect and related parameters for the linear combination of least squares means of a fixed effect.

* statements=list( list(lsmestimateList-1) <, list(lsmestimateList-2), ...>)

The lsmestimateList value can be one or more of the following:

adjust=list(method="BON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "T", method-specific-parameters)

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | list(double-1 <, double-2, ...>)

sets values of covariates.

* vars="string" | list("string-1" <, "string-2", ...>)

sets names of covariates.

byLevel=TRUE | FALSE

computes separate margins.

Default	FALSE

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

coeff=list( list(namedCoeffDef-1) <, list(namedCoeffDef-2), ...>)

The namedCoeffDef value can be one or more of the following:

coefficients=list( list(coeffEntry-1) <, list(coeffEntry-2), ...>)

The coeffEntry value can be one or more of the following:

levelIndicator=list(double-1 <, double-2, ...>)

* LMatrixValue=double

divisor=double

specifies a list of values to divide the coefficients.

* name="string"

specifies a name for every row of the multirow estimate.

confLimits=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	FALSE

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

divisor=list(double-1 <, double-2, ...>)

specifies a list of values to divide the coefficients.

joint=TRUE | FALSE

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Default	FALSE

lower=TRUE | FALSE

performs one-sided, lower-tailed inference.

Alias	lowerTailed
Default	FALSE

obsMargins=TRUE | FALSE

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	FALSE

obsMarginsData=list(castable)

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=TRUE | FALSE

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	FALSE

computedVars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

specifies data source options.

Aliases	options
Aliases	dataSource

groupBy=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

orderBy=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

singlePass=TRUE | FALSE

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	FALSE

vars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable=list(groupbytable)

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

printCoef=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	FALSE

printCorr=TRUE | FALSE

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	FALSE

printCov=TRUE | FALSE

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	FALSE

printKCoef=TRUE | FALSE

when set to True, displays the K matrix coefficients for the specified effects.

Alias	elsm
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

* terms=list( list(effect-1) <, list(effect-2), ...>)

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

upper=TRUE | FALSE

performs one-sided, upper-tailed inference.

Alias	upperTailed
Default	FALSE

margins=list( list(marginsStatement-1) <, list(marginsStatement-2), ...>)

specifies the effects and related parameters for predictive margins of fixed effects.

* statements=list( list(marginsList-1) <, list(marginsList-2), ...>)

The marginsList value can be one or more of the following:

adjust=list(method="BON" | "DUNNETT" | "GT2" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "TUKEY", method-specific-parameters)

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | list(double-1 <, double-2, ...>)

sets values of covariates.

* vars="string" | list("string-1" <, "string-2", ...>)

sets names of covariates.

byLevel=TRUE | FALSE

computes separate margins.

Default	FALSE

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

confLimits=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	FALSE

controlLevel=list("string-1" <, "string-2", ...>)

displays the differences with a control level of the specified least squares means effects.

df=double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

joint=TRUE | FALSE

when set to True, requests a joint F or chi-square test for difference of the LS-Means.

Alias	fTest
Default	FALSE

lines=TRUE | FALSE

produces 'Lines' display for pairwise LS-Means difference.

Default	FALSE

obsMargins=TRUE | FALSE

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the input data set.

Alias	OM
Default	FALSE

obsMarginsData=list(castable)

specifies the weighting scheme for LS-Means computation. It sets the coefficients to be proportional to those found in the specified data set.

Alias	OMData

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

computedOnDemand=TRUE | FALSE

when set to True, creates the computed variables when the table is loaded instead of when the action begins.

Alias	compOnDemand
Default	FALSE

computedVars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

Alias	compVars

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

computedVarsProgram="string"

specifies an expression for each computed variable that you include in the computedVars parameter.

Alias	compPgm

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

specifies data source options.

Aliases	options
Aliases	dataSource

groupBy=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the names of the variables to use for grouping results.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

groupByMode="NOSORT" | "REDISTRIBUTE"

specifies how to create groups.

Default	NOSORT

NOSORT

groups the data without sorting on each machine, and then groups the data again on the controller.

REDISTRIBUTE

transfers rows between nodes to guarantee ordering within groups. This method is slower.

importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the input table.

orderBy=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

singlePass=TRUE | FALSE

when set to True, does not create a transient table on the server. Setting this parameter to True can be efficient, but the data might not have stable ordering upon repeated runs.

Default	FALSE

vars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variables to use in the action.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the input data.

whereTable=list(groupbytable)

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)

specifies data source options.

Aliases	options
Aliases	dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)

specifies the settings for reading a table from a data source.

Alias	import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

odds=TRUE | FALSE

reports odds of levels of fixed effects if permissible by the link function.

Default	FALSE

oddsRatio=TRUE | FALSE

reports differences of LS-Means in terms of odds ratios by the link function.

Default	FALSE

printCoef=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

slice=list( list(effect-1) <, list(effect-2), ...>)

specifies effects by which to partition interaction LSMEANS effects.

Alias	sliceBy

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

sliceControlLevel=list("string-1" <, "string-2", ...>)

requests slice effects differences with a control level of each of the specified LSMEANS effects.

sliceDiff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

determines the type of simple effects differences.

Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

* terms=list( list(effect-1) <, list(effect-2), ...>)

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

weight=TRUE | FALSE

when set to True, computes the weighted predictive margins.

Default	FALSE

maxClPrint=integer

specifies the maximum levels of classification variables to print in the ClassLevels table.

Default	20

method="MIVQUE0" | "ML" | "REML"

specifies the estimation method for covariance estimation analysis.

Default	REML

MIVQUE0

performs minimum variance quadratic unbiased estimation (MIVQUE0).

ML

performs maximum likelihood estimation (ML).

REML

performs residual (restricted) maximum likelihood estimation (REML).

mmeq=TRUE | FALSE

when set to True, displays the mixed model equations table.

Default	FALSE

* model=list(mixedModelStmt)

names the dependent variable, explanatory effects, and model options.

The mixedModelStmt value can be one or more of the following:

alpha=double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

cl=TRUE | FALSE

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	FALSE

ddf=list(double-1 <, double-2, ...>)

specifies a list of the customized denominator degrees of freedom for the fixed effects.

depVars=list( list(responsevar-1) <, list(responsevar-2), ...>)

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options=list(modelopts)

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=TRUE | FALSE

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

dfMethod="CONTAIN" | "NONE" | "RESIDUAL"

specifies the degrees of freedom method.

Alias	ddfm
Default	RESIDUAL

dist="BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL"

specifies the response distribution for the model.

effects=list( list(effect-1) <, list(effect-2), ...>)

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

link="CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO"

specifies the link function for the model.

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

offset="variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

printDenseSol=TRUE | FALSE

when set to True, displays the fixed effects estimates.

Default	FALSE

printSol=TRUE | FALSE

when set to True, displays the fixed effects estimates.

Default	FALSE

trial="variable-name"

zeta=double

specifies a value to tune the estimability check.

Range	0–1

multimember=list( list(multimember-1) <, list(multimember-2), ...>)

For more information about specifying the multimember parameter, see the common multimember parameter (Appendix A: Common Parameters).

nClassLevelsPrint=integer

limits the display of class levels. The value 0 suppresses all levels.

Minimum value	0

noBlupVar=TRUE | FALSE

when set to True, suppresses computing the covariances of random-effects estimates that are used in output statistics.

Default	FALSE

noBound=TRUE | FALSE

when set to True, enforces no boundary restriction for estimating covariance parameters.

Default	FALSE

noClPrint=integer

Default	0

noInfo=TRUE | FALSE

when set to True, suppresses the display of the Model Information, Number of Observations, and Dimensions tables.

Default	FALSE

noItPrint=TRUE | FALSE

when set to True, suppresses the display of the Iteration History table.

Default	FALSE

noPrint=TRUE | FALSE

when set to True, suppresses the display of results.

Default	FALSE

noProfile=TRUE | FALSE

when set to True, includes the residual variance as one of the covariance values in the optimization iterations.

Default	FALSE

optimization=list(mixedOptimizationStmt)

specifies the technique and options for performing the optimization.

Long form	optimization=list(technique="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG")
Shortcut form	optimization="ACTIVESET" \| "CONGRA" \| "DBLDOG" \| "IPDIRECT" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"

The mixedOptimizationStmt value can be one or more of the following:

absConv=double

specifies the absolute function convergence criterion.

Minimum value	0

absConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absFConv=double

specifies the absolute function difference convergence criterion.

Minimum value	0

absFConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Minimum value	0

absGConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

absXConv=double

specifies the absolute convergence criterion.

Minimum value	0

absXConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Minimum value	0

fConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

fSize=double

specifies the value to use for both the relative function convergence criterion and the relative gradient convergence criterion.

Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Minimum value	0

gConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Default	200
Minimum value	0

maxTime=double

specifies the maximum allowed computing time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

optTol=double

defines the measure by which you can decide whether the value in the current iteration is an acceptable approximation of a local minimum.

Default	1E-05
Minimum value	0

technique="ACTIVESET" | "CONGRA" | "DBLDOG" | "IPDIRECT" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "QUANEW" | "TRUREG"

specifies the optimization technique.

ACTIVESET

uses the active set method.

CONGRA

uses the conjugate gradient method, which requires first-order derivatives.

DBLDOG

uses the double-dogleg method, which requires first-order derivatives.

IPDIRECT

uses the direct interior point method.

LBFGS

uses the Limited-memory BFGS solver, which requires first-order derivatives.

NEWRAP

uses the Newton-Raphson method with line search and ridging, which requires first- and second-order derivatives.

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed using the initial covariance values.

NRRIDG

uses the Newton-Raphson method with ridging, which requires first- and second-order derivatives.

QUANEW

uses the dual quasi-Newton method, which requires first-order derivatives.

TRUREG

uses the trust region method, which requires first- and second-order derivatives.

xConv=double

specifies the relative convergence criterion.

Minimum value	0

xConvNum=integer

specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

Minimum value	0

xSize=double

specifies the value to use for the relative convergence criterion.

Minimum value	0

output=list(mixedOutputStmt)

creates a table on the server that contains observationwise statistics, which are computed after fitting the model.

The mixedOutputStmt value can be one or more of the following:

allStats=TRUE | FALSE

when set to True, requests all available statistics.

Default	FALSE

alpha=double

specifies the significance level to use in output statistics. The default value is 0.05.

Default	0.05
Range	0–1

* casOut=list(casouttable)

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("variable-name-1" <, "variable-name-2", ...>)

lcl="string"

names the lower bound of a confidence interval for the linear predictor.

lclPA="string"

names the lower bound of a confidence interval for the marginal linear predictor.

noMiss=TRUE | FALSE

when set to True, writes only observations that are used in the analysis to the output data table. By default, output statistics for all observations are written to the output data table.

Default	FALSE

pearson="string"

names the Pearson-type residual.

pearsonPA="string"

names the marginal Pearson-type residual.

pred="string"

names the linear predictor. If no output statistics are specified, then this is the default.

Aliases	p
Aliases	predicted

predPA="string"

names the marginal linear predictor.

resid="string"

names the residual, which is calculated as ACTUAL minus PREDICTED.

Aliases	r
Aliases	residual

residPA="string"

names the marginal standard deviation of the linear predictor.

stderr="string"

names the standard deviation of the linear predictor.

stderrPA="string"

names the marginal standard deviation of the linear predictor.

student="string"

names the studentized residuals, which are the residuals divided by their standard errors.

studentPA="string"

names the marginal residual.

ucl="string"

names the upper bound of a confidence interval for the linear predictor.

uclPA="string"

names the upper bound of a confidence interval for the marginal linear predictor.

variance="string"

names the conditional variance of the response variable.

variancePA="string"

names the marginal variance of the response variable.

outputTables=list(outputTables)

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	displayOut

parms=list(mixedParmsStmt)

specifies the initial covariance values.

The mixedParmsStmt value can be one or more of the following:

hold=list(integer-1 <, integer-2, ...>)

holds all or partial covariance values.

Alias	eqcons

holdAll=TRUE | FALSE

when set to True, holds all covariance values.

Default	FALSE

initvals=list( list(list-1) <, list(list-2), ...>)

specifies the initial covariance values.

lowerB=list(double-1 <, double-2, ...>)

specifies the lower boundary for covariance values.

noIter=TRUE | FALSE

when set to True, performs no iteration for estimating covariance parameters.

Default	FALSE

parmsData=list(castable)

names the data table that contains the initial covariance values.

For more information about specifying the parmsData parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

Alias	pData

residVar=double

specifies a value for residual variance and excludes it from optimization search.

Minimum value	1E-08

upperB=list(double-1 <, double-2, ...>)

specifies the upper boundary for covariance values.

polynomial=list( list(polynomial-1) <, list(polynomial-2), ...>)

For more information about specifying the polynomial parameter, see the common polynomial parameter (Appendix A: Common Parameters).

Alias	poly

random=list( list(mixedRandomStmt-1) <, list(mixedRandomStmt-2), ...>)

specifies random effects and related options. Notice that no-intercept is inherited from the model specification where default value is FALSE, but the default value in random specification is TRUE.

The mixedRandomStmt value can be one or more of the following:

alpha=double

specifies the significance level to use for the construction of all statistics.

Default	0.05
Range	0–1

cl=TRUE | FALSE

when set to True, displays upper and lower confidence limits for the parameter estimates.

Alias	clb
Default	FALSE

covType="ANTE" | "AR" | "ARH" | "ARMA" | "CHOL" | "CS" | "CSH" | "FA" | "FA0" | "FA1" | "HF" | "TOEP" | "TOEPH" | "UC" | "UN" | "UNR" | "VC"

specifies the type of covariance structure.

Default	VC

ANTE

specifies the first-order antedependence covariance structure.

AR

specifies the first-order autoregressive covariance structure.

ARH

specifies the heterogeneous first-order autoregressive covariance structure.

ARMA

specifies the autoregressive moving average covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

FA

specifies the factor-analytic covariance structure.

FA0

specifies the no-diagonal factor-analytic covariance structure.

FA1

specifies the equal-diagonal factor-analytic covariance structure.

HF

specifies the Huynh-Feldt covariance structure.

TOEP

specifies the Toeplitz covariance structure.

TOEPH

specifies the heterogeneous Toeplitz covariance structure.

UC

specifies the uniform correlation covariance structure.

UN

specifies the unstructured covariance.

UNR

specifies the unstructured correlation covariance structure.

VC

specifies the variance components structure.

depVars=list( list(responsevar-1) <, list(responsevar-2), ...>)

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options=list(modelopts)

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=TRUE | FALSE

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

effects=list( list(effect-1) <, list(effect-2), ...>)

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group=list( list(effect-1) <, list(effect-2), ...>)

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

order=integer

specifies the order of covariance structure.

printSol=TRUE | FALSE

when set to True, displays the random-effects estimates. You can set this parameter to True by specifying the alpha or cl parameters in the model specification.

Default	FALSE

subject=list( list(effect-1) <, list(effect-2), ...>)

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

trial="variable-name"

ranks=TRUE | FALSE

when set to True, displays the table of ranks of the matrices X, (XZ), and MMEq.

Default	FALSE

repeated=list( list(mixedRepeatedStmt-1) <, list(mixedRepeatedStmt-2), ...>)

The mixedRepeatedStmt value can be one or more of the following:

covType="AR" | "CHOL" | "CS" | "CSH" | "UN" | "VC"

specifies the type of covariance structure.

Default	VC

AR

specifies the first-order autoregressive covariance structure.

CHOL

specifies the Cholesky root covariance structure.

CS

specifies the compound symmetry covariance structure.

CSH

specifies the heterogeneous compound symmetry covariance structure.

UN

specifies the unstructured covariance.

VC

specifies the variance components structure.

depVars=list( list(responsevar-1) <, list(responsevar-2), ...>)

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

The responsevar value can be one or more of the following:

name="variable-name"

names the response variable.

options=list(modelopts)

specifies a list of parameters for the response variable.

The modelopts value can be one or more of the following:

descending=TRUE | FALSE

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

effects=list( list(effect-1) <, list(effect-2), ...>)

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

group=list( list(effect-1) <, list(effect-2), ...>)

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

noConvert=TRUE | FALSE

when set to True, do not convert the repeated model to a simple one.

Default	FALSE

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

order=integer

specifies the order of covariance structure.

printR=list(integer-1 <, integer-2, ...>)

displays the blocks of the estimated R matrix.

printRC=list(integer-1 <, integer-2, ...>)

displays the Cholesky root of the estimated R matrix.

printRCI=list(integer-1 <, integer-2, ...>)

displays the inverse of the Cholesky root of the estimated R matrix.

printRCorr=list(integer-1 <, integer-2, ...>)

displays the correlation matrix that corresponds to the estimated R matrix.

printRI=list(integer-1 <, integer-2, ...>)

displays the inverse of the blocks of the estimated R matrix.

subject=list( list(effect-1) <, list(effect-2), ...>)

identifies the subjects in a mixed model.

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

trial="variable-name"

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

simpleStat=TRUE | FALSE

when set to True, displays the Descriptive Statistics table.

Default	FALSE

singChol=double

tunes the singularity criterion for Cholesky decompositions.

Range	0–1

singRes=double

tunes the singularity criterion for the residual variance.

Range	0–1

singular=double

tunes the general singularity criterion.

Range	0–1

slice=list( list(sliceStatement-1) <, list(sliceStatement-2), ...>)

specifies the effects and related parameters for a partitioned analysis of the LS-Means for an interaction.

* statements=list( list(sliceList-1) <, list(sliceList-2), ...>)

The sliceList value can be one or more of the following:

adjust=list(method="BON" | "DUNNETT" | "GT2" | "NELSON" | "NONE" | "SCHEFFE" | "SIDAK" | "SIMULATE" | "SMM" | "T" | "TUKEY", method-specific-parameters)

determines the adjustment method for multiple comparisons of LS-Means differences.

The value that you specify for the method parameter determines the other parameters that apply.

alpha=double

displays a t-type confidence interval for each of the least squares means with this confidence level.

Default	0.05
Range	0–1

at="MEANS" | {lsmeansOptionAt}

modifies covariate values in computing LS-Means. By default, all covariate effects are set equal to their mean values for computation of LS-Means.

The lsmeansOptionAt value can be one or more of the following:

* vals=double | list(double-1 <, double-2, ...>)

sets values of covariates.

* vars="string" | list("string-1" <, "string-2", ...>)

sets names of covariates.

confLimits=TRUE | FALSE

when set to True, constructs t-type confidence limits for each of the least squares means.

Alias	cl
Default	FALSE

controlLevel=list("string-1" <, "string-2", ...>)

displays the differences with a control level of the specified least squares means effects.

df=double

specifies the degrees of freedom for the t test and confidence limits.

Minimum value	0

diff="ALL" | "ANOM" | "CONTROL" | "CONTROLL" | "CONTROLU" | "NONE"

displays differences of the least squares means.

Alias	pdiff
Default	ALL

ALL

displays all pairwise differences for the least squares means.

ANOM

displays the differences between each least squares mean and the average of the least squares means.

CONTROL

displays the differences with the first level for each of the specified least squares means effects as a control level.

CONTROLL

displays one-tailed results and tests whether the noncontrol levels are significantly smaller than the control level.

CONTROLU

displays one-tailed results and tests whether the noncontrol levels are significantly larger than the control level.

NONE

the difference type is not specified.

fTest=TRUE | FALSE

when set to True, requests the F test for testing the mutual equality of the estimable functions in the partitioned analysis of LS-Means.

Default	TRUE

lines=TRUE | FALSE

produces 'Lines' display for pairwise LS-Means difference.

Default	FALSE

means=TRUE | FALSE

specifies to use the covariates means in the partitioned analysis of LS-Means.

Default	FALSE

oddsRatio=TRUE | FALSE

reports differences of LS-Means in terms of odds ratios by the link function.

Default	FALSE

printCoef=TRUE | FALSE

when set to True, displays the matrix coefficients for all effects.

Alias	e
Default	FALSE

printCorr=TRUE | FALSE

when set to True, displays the estimated correlation matrix of the least squares means.

Alias	corr
Default	FALSE

printCov=TRUE | FALSE

when set to True, displays the estimated covariance matrix of the least squares means.

Alias	cov
Default	FALSE

singular=double

tunes the estimability checking.

Default	0.0001
Range	0–1

sliceBy=list( list(slicebyDef-1) <, list(slicebyDef-2), ...>)

specifies effects by which to partition interaction LSMEANS effects.

The slicebyDef value can be one or more of the following:

levels=list("string-1" <, "string-2", ...>)

* term=list(effect)

The effect value can be one or more of the following:

interaction="BAR" | "CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

eliminates interaction effects whose order is higher than the specified integer value when used in conjunction with the BAR interaction.

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

* term=list(effect)

specifies effects in the model for the estimates of the least squares means.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

spline=list( list(spline-1) <, list(spline-2), ...>)

expands variables into spline bases whose form depends on the specified parameters.

For more information about specifying the spline parameter, see the common spline parameter (Appendix A: Common Parameters).

store=list(casouttable)

Stores linear mixed models to a blob (binary large object).

Alias	saveState

Long form	store=list(name="table-name")
Shortcut form	store="table-name"

The casouttable value can be one or more of the following:

caslib="string"

specifies the name of the caslib for the output table.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default	0
Minimum value	0

memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default	INHERIT

DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=TRUE | FALSE

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default	FALSE

replace=TRUE | FALSE

when set to True, overwrites an existing table that has the same name.

Default	FALSE

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table=list(castable)

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

test=list( list(testStatement-1) <, list(testStatement-2), ...>)

specifies the effects and related parameters for hypothesis test of fixed effects.

* statements=list( list(testList-1) <, list(testList-2), ...>)

The testList value can be one or more of the following:

chiSq=TRUE | FALSE

requests a chi-square test in addition to the F test.

Default	FALSE

df=double

specifies the denominator degrees of freedom for the hypothesis test.

Minimum value	0

eType=list(integer-1 <, integer-2, ...>)

specifies the type of coefficients to display.

hType=list(integer-1 <, integer-2, ...>)

specifies the type of hypothesis test to perform on the specified effects.

Default	3

* terms=list( list(effect-1) <, list(effect-2), ...>)

specifies model effects for the hypothesis test.

The effect value can be one or more of the following:

interaction="CROSS" | "NONE"

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

nest=list("string-1" <, "string-2", ...>)

* vars=list("string-1" <, "string-2", ...>)

specifies the variables to use in defining a term of the effect. You must specify at least one variable.

timing=TRUE | FALSE

when set to True, displays the Timing table.

Default	FALSE

weight="variable-name"

names the numeric variable to use in performing a weighted analysis of the data.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

Parameters for method="BON"

No parameters apply when you specify BON.

Parameters for method="DUNNETT"

No parameters apply when you specify DUNNETT.

Parameters for method="GT2" | "SMM"

No parameters apply when you specify GT2.

Parameters for method="NELSON"

No parameters apply when you specify NELSON.

Parameters for method="NONE"

No parameters apply when you specify NONE.

Parameters for method="SCHEFFE"

No parameters apply when you specify SCHEFFE.

Parameters for method="SIDAK"

No parameters apply when you specify SIDAK.

Parameters for method="SIMULATE"

ACC=double

specifies the target accuracy radius confidence interval in ADJUST=SIMULATE.

Default	0.005
Range	0–1

CV=TRUE | FALSE

specifies CV option in ADJUST=SIMULATE.

Default	FALSE

epsilon=double

specifies the value for confidence interval in ADJUST=SIMULATE.

Alias	EPS
Default	0.01
Range	0–1

nSample=64-bit-integer

specifies the sample size in ADJUST=SIMULATE.

Alias	nSamp
Default	12604
Minimum value	0

report=TRUE | FALSE

specifies REPORT option in ADJUST=SIMULATE.

Default	FALSE

seed=64-bit-integer

specifies the seed for random number generation in ADJUST=SIMULATE.

Parameters for method="T"

No parameters apply when you specify T.

Parameters for method="TUKEY"

No parameters apply when you specify TUKEY.

Last updated: March 05, 2026