Regression Action Set

Provides actions for fitting linear, generalized linear, and logistic models

logistic Action

Fits logistic regression models.

CASL Syntax
Summary: Input and Output Tables
Parameter Descriptions

CASL Syntax

regression.logistic <result=results> <status=rc> /

alpha=double,

applyRowOrder=TRUE | FALSE,

association=TRUE | FALSE,

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

binEps=double,

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",

split=TRUE | FALSE,

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",

split=TRUE | FALSE

classLevelsPrint=TRUE | FALSE,

clb=TRUE | FALSE | "WALD" | "PL",

code={

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"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

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

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=TRUE | FALSE,

fmtWdth=integer,

indentSize=integer,

intoCutPt=double,

iProb=TRUE | FALSE,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

pCatAll=TRUE | FALSE,

tabForm=TRUE | FALSE

collection={{

details=TRUE | FALSE,

name="string",

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

}, {...}},

corrB=TRUE | FALSE,

covB=TRUE | FALSE,

ctable={

accuracy="string",

allStats=TRUE | FALSE,

casOut={

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

replace=TRUE | FALSE

replication=integer

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

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

fnf="string",

fpf="string",

lift="string",

misclass="string",

nocounts=TRUE | FALSE,

npv="string",

pc="string",

ppv="string",

tnf="string",

tpf="string"

display={

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

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

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

fitData=TRUE | FALSE,

freq="variable-name",

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

lackfit={

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

df=double,

dfReduce=integer,

nGroups=integer,

noncentrality=double,

powerAdj=TRUE | FALSE

lsmeans={{

statements={{

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,

e=TRUE | FALSE,

singular=double,

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

}, {...}}

}, {...}},

maxOptBatch=64-bit-integer | "AUTO",

maxResponseLevels=integer,

model={

center=TRUE | FALSE,

centerlasso=TRUE | FALSE,

clb=TRUE | FALSE,

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

dist="BERNOULLI" | "BINOMIAL" | "MULTINOMIAL",

effects={{

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

maxInteract=integer,

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

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

}, {...}},

entry="variable-name",

include=integer | {{effect-1} <, {effect-2}, ...>},

informative=TRUE | FALSE,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

link="CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT",

noint=TRUE | FALSE,

offset="variable-name",

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

ss3=TRUE | FALSE,

start=integer | {{effect-1} <, {effect-2}, ...>},

trial="variable-name"

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", ...>}

}, {...}},

multipass=TRUE | FALSE,

nClassLevelsPrint=integer,

noCheck=TRUE | FALSE,

nominals={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

normalize=TRUE | FALSE,

nostderr=TRUE | FALSE,

noxpx=TRUE | FALSE,

oddsratio={

alpha=double,

at={{

level="ALL" | "REF" | "string" | {"string-1" <, "string-2", ...>},

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

var="variable-name"

}, {...}},

cl="PL" | "WALD",

diff="ALL" | "REF",

unit={{

stderr=TRUE | FALSE,

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

var="variable-name"

}, {...}},

vars={{

at={{orSpecAt-1} <, {orSpecAt-2}, ...>},

stderr=TRUE | FALSE,

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

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

}, {...}}

optimization={

absConv=double,

absFConv=double,

absGConv=double,

absXConv=double,

corrections=integer,

fConv=double,

fConv2=double,

gConv=double,

gConv2=double,

inParmEst={

caslib="string"

computedOnDemand=TRUE | FALSE

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"

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"

}

itHist="NONE" | "SUMMARY",

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

singRes=double,

xConv=double

output={

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", ...>}

cBar="string",

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

difChisq="string",

difDev="string",

h="string",

into="string",

intoCutpt=double,

ipred="string",

lcl="string",

lclm="string",

level="string",

obscat=TRUE | FALSE,

post="string",

pred="string",

predprobs=TRUE | FALSE,

resChi="string",

resDev="string",

resLik="string",

resRaw="string",

resWork="string",

role="string",

stdResChi="string",

stdResDev="string",

stdXBeta="string",

ucl="string",

uclm="string",

xBeta="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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED",

partByFrac={

seed=integer,

test=double,

validate=double

partByVar={

name="variable-name",

test="string",

train="string",

validate="string"

partFit=TRUE | FALSE,

plConv=double,

plMaxIter=integer,

plSingular=double,

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", ...>}

}, {...}},

repeated={{

converge=double,

corrb=TRUE | FALSE,

corrtype="AR" | "EXCH" | "IND" | "MDEP" | "UN",

corrw=TRUE | FALSE,

covb=TRUE | FALSE,

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

ecorrb=TRUE | FALSE,

ecovb=TRUE | FALSE,

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", ...>}

}, {...}},

maxIter=64-bit-integer,

mcorrb=TRUE | FALSE,

mcovb=TRUE | FALSE,

mdepm=64-bit-integer,

modelse=TRUE | FALSE,

printmle=TRUE | FALSE,

subject={{

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

maxInteract=integer,

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

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

}, {...}},

trial="variable-name"

}, {...}},

restore={

caslib="string",

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

name="table-name",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

seed=64-bit-integer,

selection={

candidates=integer | "ALL",

details="ALL" | "NONE" | "STEPS" | "SUMMARY",

elasticNetOptions={

absFConv=double

fConv=double

gConv=double

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

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

numLambda=integer

rho=double

solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

fast=TRUE | FALSE,

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

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

maxEffects=integer,

maxSteps=integer,

minEffects=integer,

orderSelect=TRUE | FALSE,

plots=TRUE | FALSE,

relaxed=TRUE | FALSE,

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

slEntry=double,

slStay=double,

stopHorizon=integer

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", ...>}

}, {...}},

ss3=TRUE | FALSE,

stb=TRUE | FALSE,

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"

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

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"

}

target="string",

useLastIter=TRUE | FALSE,

weight="variable-name",

weightNorm=TRUE | FALSE

;

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
optimization	inParmEst	specifies the technique and options for performing the optimization.
restore	—	restores regression models from a binary large object (BLOB).
table	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
ctable	casOut	creates the classification table.
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 regression models to a binary large object (BLOB).

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all confidence intervals.

Default	0.05
Range	(0, 1)

applyRowOrder=TRUE | FALSE

when set to True, uses the available groupBy and orderBy information to group and order the data.

Default	FALSE

association=TRUE | FALSE

when sent to True, creates the association table.

Default	FALSE

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

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

binEps=double

specifies the precision of the predicted probabilities that are used for classification.

Default	1E-05
Range	0–1

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

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

For more information about class subparameters, see class Parameter (Shared Concepts).

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

Alias	classVars

classGlobalOpts={classopts}

lists options that apply to all classification variables.

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

classLevelsPrint=TRUE | FALSE

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

Default	TRUE

clb=TRUE | FALSE | "WALD" | "PL"

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

code={aircodegen}

writes SAS DATA step code for computing predicted values of the fitted model

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

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

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

For more information, see Collection Effects (Shared Concepts).

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.

corrB=TRUE | FALSE

when set to True, displays the correlation matrix of the parameters.

Default	FALSE

covB=TRUE | FALSE

when set to True, displays the covariance matrix of the parameters.

Default	FALSE

ctable={ctableOptions}

creates the classification table.

For more information, see Classification Table and ROC Curves .

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

accuracy="string"

includes and names the accuracy in the classification table.

allStats=TRUE | FALSE

when set to True, requests all available statistics.

Default	FALSE

casOut={casouttable}

specifies the settings for an output table.

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

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

specifies the list of variables to create indexes for in the output data.

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

maxMemSize=64-bit-integer

specifies the maximum amount of memory, in bytes, that each thread should allocate for in-memory blocks before converting to a memory-mapped file. Files are written in the directories that are specified in the CAS_DISK_CACHE environment variable.

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

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.

replace=TRUE | FALSE

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

Default	FALSE

replication=integer

specifies the number of copies of the table to make for fault tolerance. Larger values result in slower performance and use more memory, but provide high availability for data in the event of a node failure. Data redundancy applies to distributed servers only.

Default	1
Minimum value	0

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.

threadBlockSize=64-bit-integer

specifies the number of bytes to use for blocks in the output table. The blocks are read by threads. Gradually increase this value when you have a large table with millions or billions of rows and you are tuning for performance. Larger values can increase performance with indexed tables. However, if the value is too large, then you can cause thread starvation due to too few blocks for threads to work on.

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

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

specifies one or more expressions for subsetting the output data. When multiple expressions are specified, the expressions are effectively combined using AND to form the final output filter. If an expression contains quoted values, use nested quotation marks.

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

specifies cutpoints for the classification table.

fnf="string"

includes and names the false negative fraction in the classification table.

fpf="string"

includes and names the false positive fraction (1-specificity) in the classification table.

lift="string"

includes and names the lift in the classification table.

misclass="string"

includes and names the misclassification rate in the classification table.

nocounts=TRUE | FALSE

when set to True, removes counts from the classification table.

Default	FALSE

npv="string"

includes and names the negative predictive value in the classification table.

pc="string"

includes and names the percent correct in the classification table.

ppv="string"

includes and names the positive predictive value (precision) in the classification table.

tnf="string"

includes and names the true negative fraction (specificity) in the classification table.

tpf="string"

includes and names the true positive fraction (recall, sensitivity) in the classification table.

display={displayTables}

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

For more information about display subparameters, see display Parameter (Shared Concepts).

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

fitData=TRUE | FALSE

when set to True, specifies that the data to be scored were also used to fit the model.

Default	FALSE

freq="variable-name"

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

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

specifies variables to use for analysis.

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

Alias	input

lackfit={lackfitOptions}

creates the Hosmer and Lemeshow tables.

For more information, see The Hosmer-Lemeshow Goodness-of-Fit Test .

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

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

specifies cutpoints for the Hosmer and Lemeshow partitions.

df=double

specifies the degrees of freedom to use for the Hosmer and Lemeshow test.

Minimum value	0

dfReduce=integer

specifies the reduction in degrees of freedom for the Hosmer and Lemeshow test.

Default	2
Minimum value	0

nGroups=integer

specifies the maximum number of groups to create for the Hosmer and Lemeshow test.

Default	10
Minimum value	5

noncentrality=double

specifies the noncentrality parameter for the Hosmer and Lemeshow test.

Default	0
Minimum value	0

powerAdj=TRUE | FALSE

when set to True, adjusts the number of groups so that the Hosmer and Lemeshow test can maintain power.

Default	FALSE

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

specifies the effects and subparameters for least squares means.

For more information, see lsmeans Parameter (Shared Concepts).

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

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

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.

For more information about the methods and the method-specific-parameters, see the description of the adjust subparameter in the lsmeans parameter (Shared Concepts).

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.

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

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

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

displays differences of the least squares means.

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

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

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

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

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

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", ...>}

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.

maxOptBatch=64-bit-integer | "AUTO"

controls the number of observations processed in one batch.

For more information, see the description of the pageObs parameter in Memory Usage .

Alias	pageObs

maxResponseLevels=integer

specifies the maximum number of levels allowed for a multinomial response.

Default	100
Minimum value	2

model={logisticModel}

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

For information about model specification, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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

center=TRUE | FALSE

when set to TRUE, centers and scales continuous covariates.

Default	FALSE

centerlasso=TRUE | FALSE

when set to TRUE, centers and scales covariates, including categorical covariates, for the LASSO method.

Default	TRUE

clb=TRUE | FALSE

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

Default	FALSE

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.

dist="BERNOULLI" | "BINOMIAL" | "MULTINOMIAL"

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.

For more information, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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.

entry="variable-name"

specifies the entry variable.

include=integer | {{effect-1} <, {effect-2}, ...>}

specifies effects to include at the start of the selection process for the specified selection method. Included effects are never dropped during the selection process. If you specify n, where n is a positive integer, then the included effects consist of the first n effects of the model specification.

The effect value is specified as follows:

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.

informative=TRUE | FALSE

when set to True, models missing values by using extra model effects. These effects consist of dummy variables that take the value 1 when the value of a continuous model variable involved in the effect is missing, and take the value 0 otherwise. The missing value in the original model effect is replaced by the average value of the effect for the nonmissing values. For classification variables, missing values are treated as valid levels.

For more information, see Informative Missingness (Shared Concepts).

Default	FALSE

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

link="CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT"

specifies the link function for the model.

For more information, see the LINK= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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.

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

specifies the priors for each response level, which is used for computing the posterior predicted value.

For more information, see the PRIOR= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

ss3=TRUE | FALSE

when set to True, performs Type 3 effect tests. Under full-rank parameterizations or models with constructed effects, Type 3 effect tests are replaced by joint tests. The joint test for an effect is a test that all the parameters associated with that effect are zero. Such joint tests might not be equivalent to Type 3 effect tests under GLM parameterization.

Default	FALSE

start=integer | {{effect-1} <, {effect-2}, ...>}

specifies effects to use to begin the selection process in the FORWARD, FORWARDSWAP, and STEPWISE selection methods. If you specify n, where n is a positive integer, then the starting model consists of the first n effects of the model specification.

The effect value is specified as follows:

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"

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).

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, see Multimember Effects (Shared Concepts).

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

multipass=TRUE | FALSE

when set to True, levelizes the input data table everytime it is read.

Default	FALSE

nClassLevelsPrint=integer

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

Minimum value	0

noCheck=TRUE | FALSE

when set to True, does not check logistic models for separation.

For more information, see Existence of Maximum Likelihood Estimates .

Default	FALSE

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

specifies nominal variables to use for analysis.

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

Alias	nominal

normalize=TRUE | FALSE

when set to True, divides the log likelihood by the total number of observations during the optimization.

Default	TRUE

nostderr=TRUE | FALSE

when set to True, the covariance matrix and any statistic that depends on it are not computed.

Default	FALSE

noxpx=TRUE | FALSE

when set to True, does not compute X'WX and Hessian matrices, and disables all methods and suppresses all outputs that rely on them.

Default	FALSE

oddsratio={oddsratioOptions}

creates a table that compares subpopulations by using odds ratios.

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

alpha=double

specifies the significance level of the confidence limits.

Default	0.05
Range	(0, 1)

at={{orAtOpts-1} <, {orAtOpts-2}, ...>}

changes the default fixed values or levels for covariates that interact with the odds ratio variable.

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

level="ALL" | "REF" | "string" | {"string-1" <, "string-2", ...>}

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

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

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* var="variable-name"

specifies a covariate that interacts with the odds ratio variable.

cl="PL" | "WALD"

specifies which types of confidence intervals to compute.

Default	WALD

PL

computes profile-likelihood confidence limits.

WALD

computes Wald confidence limits.

diff="ALL" | "REF"

specifies which pairs of response levels to compare.

Default	REF

ALL

specifies all levels of the classification variable.

REF

specifies the reference level of the classification variable.

unit={{orUnitOpts-1} <, {orUnitOpts-2}, ...>}

changes the default units of change for continuous odds ratio variables.

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

stderr=TRUE | FALSE

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	FALSE

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

specifies units of change for a continuous odds ratio variable.

* var="variable-name"

specifies a continuous odds ratio variable.

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

specifies variables for which odds ratios are computed.

Aliases	oddsratios
Aliases	oddsratio

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

at={{orSpecAt-1} <, {orSpecAt-2}, ...>}

specifies fixed values or levels for covariates that interact with the odds ratio variable.

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

level="ALL" | "REF" | "string" | {"string-1" <, "string-2", ...>}

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

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

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* var="variable-name"

specifies a covariate that interacts with the odds ratio variable.

stderr=TRUE | FALSE

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	FALSE

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

specifies units of change for a continuous odds ratio variable.

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

specifies variables for which odds ratios are computed.

optimization={optimizationStatement}

specifies the technique and options for performing the optimization.

For more information, see the description of the parameters in Optimization Parameters (Shared Concepts).

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	absTol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

absXConv=double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

corrections=integer

specifies the number of corrections used in the LBFGS update.

Alias	correction
Default	20
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

inParmEst={castable}

specifies the input initial parameter estimates data table that contains starting values for the optimization.

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

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.

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.

itHist="NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

singRes=double

specifies the singularity criterion for the residual variance.

Range	0–1

technique="CONGRA" | "DBLDOG" | "DUQUANEW" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "TRUREG"

specifies the optimization technique.

For more information, see Choosing an Optimization Algorithm (Shared Concepts).

Alias	tech
Default	NRRIDG

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

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 at the starting parameter values.

NRRIDG

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

TRUREG

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

xConv=double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output={logisticOutputStatement}

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

For more information about the available statistics, see Predicted Probabilities and Regression Diagnostics .

For more information, see OUTPUT Statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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

alpha=double

specifies the significance level to use for the construction of confidence intervals. By default, this is set to the global significance level.

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).

cBar="string"

names the confidence interval displacement, which measures the overall change in the global regression estimates that can be attributed to deleting the individual observation.

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.

difChisq="string"

names the change in the Pearson chi-square statistic that can be attributed to deleting the individual observation.

difDev="string"

names the change in the deviance that can be attributed to deleting the individual observation.

h="string"

names the leverage of the observation.

Alias	hatDiag

into="string"

names the predicted response level.

intoCutpt=double

specifies the predicted event probability that determines the predicted binary response level.

Default	0.5

ipred="string"

names the individual predicted value for a cumulative link. If you do not specify any output statistics, then by default the predicted value is named _IPRED_.

Aliases	ip
Aliases	individual

lcl="string"

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

Aliases	lowerXBeta
Aliases	lowerLinP

lclm="string"

names the lower bound of a confidence interval for the mean.

Aliases	lower
Aliases	lowerMean

level="string"

names the ordered response level.

obscat=TRUE | FALSE

when set to True, computes multinomial output statistics at the observed response level.

Default	FALSE

post="string"

names the posterior predicted value.

pred="string"

names the predicted value. If you do not specify any output statistics, then by default the predicted value is named _PRED_.

Aliases	p
	predicted
	iLink
	mean

predprobs=TRUE | FALSE

when set to True, displays requested multinomial predicted probabilities as separate variables.

Default	FALSE

resChi="string"

names the Pearson chi-square residual.

Aliases	pearson
Aliases	pears

resDev="string"

names the deviance residual.

Alias	devResid

resLik="string"

names the likelihood residual (likelihood displacement).

Aliases	likeDist
	ld
	resLike

resRaw="string"

names the raw residual.

Aliases	r
	resid
	residual
	rawResid

resWork="string"

names the working residual.

role="string"

identifies the training, validation, and test roles for the observations.

stdResChi="string"

names the standardized Pearson chi-square residual.

Aliases	adjPearson
Aliases	adjPears

stdResDev="string"

names the standardized deviance residual.

Alias	stdDevResid

stdXBeta="string"

names the standard error of the linear predictor.

Alias	stdP

ucl="string"

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

Aliases	upperXBeta
Aliases	upperLinP

uclm="string"

names the upper bound of a confidence interval for the mean.

Aliases	upper
Aliases	upperMean

xBeta="string"

names the linear predictor.

Alias	linP

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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED"

specifies whether to add raw and formatted values of classification variables in the ParameterEstimates table.

Default	RAW

partByFrac={partByFracStatement}

specifies the fractions of the data to be used for validation and testing.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

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

seed=integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

test=double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

validate=double

randomly assigns the specified proportion of observations in the input table to the validation role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Alias	valid
Range	0–1

partByVar={partByVarStatement}

names the variable and its values used to partition the data into training, validation, and testing roles.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

Long form	partByVar={name="variable-name"}
Shortcut form	partByVar="variable-name"

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

* name="variable-name"

names the variable in the input table whose values are used to assign roles to each observation.

test="string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

train="string"

specifies the formatted value of the variable that is used to assign observations to the training role. If you do not specify the train parameter, then all observations whose roles are not determined by the test and validate parameters are assigned to training.

validate="string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

partFit=TRUE | FALSE

when set to True, displays the fit statistics that are produced when your data are partitioned.

For more information, see Partition Fit Statistics .

Default	FALSE

plConv=double

specifies the convergence criterion for the profile likelihood computations.

Default	0.0001
Range	0–1

plMaxIter=integer

specifies the maximum number of iterations for the profile likelihood computations.

Default	25
Minimum value	0

plSingular=double

specifies the tolerance for testing singularity for profile likelihood computations.

Range	0–1

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, see Polynomial Effects (Shared Concepts).

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

Alias	poly

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

specifies the options for repeated measures analysis.

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

converge=double

specifies the convergence criterion for repeated measures analysis.

Default	0.0001
Minimum value	0

corrb=TRUE | FALSE

when set to True, displays the estimated model-based and empirical correlation matrices of the parameters.

Default	FALSE

corrtype="AR" | "EXCH" | "IND" | "MDEP" | "UN"

specifies the type of correlation structure.

Alias	covtype
Default	IN

AR

specifies the first-order autoregressive correlation structure.

EXCH

specifies the compound symmetry correlation structure.

Alias	CS

IND

specifies the independence correlation structure.

Alias	IN

MDEP

specifies the m-dependent correlation structure.

UN

specifies the unstructured correlation structure.

corrw=TRUE | FALSE

when set to True, displays the estimated working correlation matrix.

Default	FALSE

covb=TRUE | FALSE

when set to True, displays the estimated model-based and empirical covariance matrices of the parameters.

Default	FALSE

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"

ecorrb=TRUE | FALSE

when set to True, displays the estimated empirical correlation matrix of the parameters.

Default	FALSE

ecovb=TRUE | FALSE

when set to True, displays the estimated empirical covariance matrix of the parameters.

Default	FALSE

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}, ...>}

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 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.

maxIter=64-bit-integer

specifies the maximum number of iterations for repeated measures analysis.

Default	50
Minimum value	0

mcorrb=TRUE | FALSE

when set to True, displays the estimated model-based correlation matrix of the parameters.

Default	FALSE

mcovb=TRUE | FALSE

when set to True, displays the estimated model-based covariance matrix of the parameters.

Default	FALSE

mdepm=64-bit-integer

specifies the order of the m-dependent correlation structure.

Default	1
Minimum value	1

modelse=TRUE | FALSE

produces a parameter estimates table that displays and uses the model-based standard errors.

Default	FALSE

printmle=TRUE | FALSE

produces the parameter estimates table from the initial stage of estimation.

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"

restore={castable}

restores regression models from a binary large object (BLOB).

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

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.

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

* name="table-name"

specifies the name of the input table.

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.

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

selection={selectionStatement}

specifies the method and options for performing model selection.

For more information, see selection Parameter (Shared Concepts).

Long form	selection={method="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"

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

candidates=integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

choose="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

For more information, see the discussion of the choose subparameter (Shared Concepts).

details="ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

For more information, see the description of the details subparameter (Shared Concepts).

Default	SUMMARY

elasticNetOptions={enOptions}

specifies options to use in performing elastic net selection methods.

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

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	abstol
Default	1E-08
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

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

specifies the regularization parameters in the elastic net selection method.

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

specifies the elastic net mixing parameter.

numLambda=integer

specifies the number of regularization parameters in the elastic net selection method.

Alias	nLambda
Default	0
Minimum value	0

rho=double

specifies the scaling factor to use in computing minimum regularization parameter.

Range	(0, 1)

solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

fast=TRUE | FALSE

implements the computational algorithm of Lawless and Singhal (1978) to compute a first-order approximation to the remaining slope estimates for each subsequent elimination of a variable from the model during backward selection for generalized linear models.

Default	FALSE

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

specifies whether and how to apply the model hierarchy requirement. Model hierarchy refers to the requirement that, for any term to be in the model, all model effects that are contained in the term must be present in the model.

For more information, see the description of the hierarchy subparameter (Shared Concepts).

Default	DEFAULT

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

specifies the coefficients in the relaxed LASSO method.

maxEffects=integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

maxSteps=integer

specifies the maximum number of selection steps to perform.

method="BACKWARD" | "ELASTICNET" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE"

specifies the model selection method.

For more information, see Model Selection Methods (Shared Concepts).

Default	STEPWISE

minEffects=integer

specifies the minimum number of effects in any model to consider during backward selection.

orderSelect=TRUE | FALSE

when set to True, shows effects in parameter estimates tables in the order in which they were added to the model.

Default	FALSE

plots=TRUE | FALSE

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

For more information about the plots you can create, see Model Selection Plots (Shared Concepts in SAS Visual Statistics: Procedures).

Default	FALSE

relaxed=TRUE | FALSE

when set to True, applies the relaxed LASSO method.

Default	FALSE

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

For more information, see the discussion of the select subparameter (Shared Concepts).

slEntry=double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

slStay=double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

stop="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

For more information, see the discussion of the stop subparameter (Shared Concepts).

stopHorizon=integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

For more information, see the description of the stopHorizon subparameter (Shared Concepts).

Default	3

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

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

For more information, see Spline Effects (Shared Concepts).

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

ss3=TRUE | FALSE

Default	FALSE

stb=TRUE | FALSE

when set to True, produces standardized regression coefficients. A standardized regression coefficient is computed by dividing a parameter estimate by the ratio of the sample standard deviation of the dependent variable to the sample standard deviation of the regressor.

Default	FALSE

store={casouttable}

stores regression models to a binary large object (BLOB).

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.

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

specifies text to store that gets displayed when you restore the model.

Alias	storenote

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).

target="string"

specifies the target variable to use for analysis.

useLastIter=TRUE | FALSE

when equal to 1, displays all tables even if there is an optimization error.

Default	FALSE

weight="variable-name"

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

weightNorm=TRUE | FALSE

adjusts the weights so the total weight equals the total frequency.

Default	FALSE

logistic Action

Fits logistic regression models.

Lua Syntax
Summary: Input and Output Tables
Parameter Descriptions

Lua Syntax

results, info = s:regression_logistic{

alpha=double,

applyRowOrder=true | false,

association=true | false,

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

binEps=double,

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",

split=true | false,

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",

split=true | false

classLevelsPrint=true | false,

clb=true | false | "WALD" | "PL",

code={

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"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

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

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=true | false,

fmtWdth=integer,

indentSize=integer,

intoCutPt=double,

iProb=true | false,

labelId=integer,

lineSize=integer,

noTrim=true | false,

pCatAll=true | false,

tabForm=true | false

collection={{

details=true | false,

name="string",

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

}, {...}},

corrB=true | false,

covB=true | false,

ctable={

accuracy="string",

allStats=true | false,

casOut={

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

replace=true | false

replication=integer

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

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

fnf="string",

fpf="string",

lift="string",

misclass="string",

nocounts=true | false,

npv="string",

pc="string",

ppv="string",

tnf="string",

tpf="string"

display={

caseSensitive=true | false,

exclude=true | false,

excludeAll=true | false,

keyIsPath=true | false,

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

pathType="LABEL" | "NAME",

traceNames=true | false

fitData=true | false,

freq="variable-name",

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

lackfit={

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

df=double,

dfReduce=integer,

nGroups=integer,

noncentrality=double,

powerAdj=true | false

lsmeans={{

statements={{

alpha=double,

at="MEANS" | {lsmeansOptionAt},

cl=true | false,

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

corr=true | false,

cov=true | false,

e=true | false,

singular=double,

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

}, {...}}

}, {...}},

maxOptBatch=64-bit-integer | "AUTO",

maxResponseLevels=integer,

model={

center=true | false,

centerlasso=true | false,

clb=true | false,

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

dist="BERNOULLI" | "BINOMIAL" | "MULTINOMIAL",

effects={{

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

maxInteract=integer,

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

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

}, {...}},

entry="variable-name",

include=integer | {{effect-1} <, {effect-2}, ...>},

informative=true | false,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

link="CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT",

noint=true | false,

offset="variable-name",

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

ss3=true | false,

start=integer | {{effect-1} <, {effect-2}, ...>},

trial="variable-name"

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", ...>}

}, {...}},

multipass=true | false,

nClassLevelsPrint=integer,

noCheck=true | false,

nominals={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

normalize=true | false,

nostderr=true | false,

noxpx=true | false,

oddsratio={

alpha=double,

at={{

level="ALL" | "REF" | "string" | {"string-1" <, "string-2", ...>},

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

var="variable-name"

}, {...}},

cl="PL" | "WALD",

diff="ALL" | "REF",

unit={{

stderr=true | false,

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

var="variable-name"

}, {...}},

vars={{

at={{orSpecAt-1} <, {orSpecAt-2}, ...>},

stderr=true | false,

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

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

}, {...}}

optimization={

absConv=double,

absFConv=double,

absGConv=double,

absXConv=double,

corrections=integer,

fConv=double,

fConv2=double,

gConv=double,

gConv2=double,

inParmEst={

caslib="string"

computedOnDemand=true | false

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"

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"

}

itHist="NONE" | "SUMMARY",

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

singRes=double,

xConv=double

output={

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", ...>}

cBar="string",

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

difChisq="string",

difDev="string",

h="string",

into="string",

intoCutpt=double,

ipred="string",

lcl="string",

lclm="string",

level="string",

obscat=true | false,

post="string",

pred="string",

predprobs=true | false,

resChi="string",

resDev="string",

resLik="string",

resRaw="string",

resWork="string",

role="string",

stdResChi="string",

stdResDev="string",

stdXBeta="string",

ucl="string",

uclm="string",

xBeta="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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED",

partByFrac={

seed=integer,

test=double,

validate=double

partByVar={

name="variable-name",

test="string",

train="string",

validate="string"

partFit=true | false,

plConv=double,

plMaxIter=integer,

plSingular=double,

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", ...>}

}, {...}},

repeated={{

converge=double,

corrb=true | false,

corrtype="AR" | "EXCH" | "IND" | "MDEP" | "UN",

corrw=true | false,

covb=true | false,

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

ecorrb=true | false,

ecovb=true | false,

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", ...>}

}, {...}},

maxIter=64-bit-integer,

mcorrb=true | false,

mcovb=true | false,

mdepm=64-bit-integer,

modelse=true | false,

printmle=true | false,

subject={{

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

maxInteract=integer,

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

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

}, {...}},

trial="variable-name"

}, {...}},

restore={

caslib="string",

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

name="table-name",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

seed=64-bit-integer,

selection={

candidates=integer | "ALL",

details="ALL" | "NONE" | "STEPS" | "SUMMARY",

elasticNetOptions={

absFConv=double

fConv=double

gConv=double

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

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

numLambda=integer

rho=double

solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

fast=true | false,

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

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

maxEffects=integer,

maxSteps=integer,

minEffects=integer,

orderSelect=true | false,

plots=true | false,

relaxed=true | false,

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

slEntry=double,

slStay=double,

stopHorizon=integer

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", ...>}

}, {...}},

ss3=true | false,

stb=true | false,

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"

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

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"

}

target="string",

useLastIter=true | false,

weight="variable-name",

weightNorm=true | false

}

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
optimization	inParmEst	specifies the technique and options for performing the optimization.
restore	—	restores regression models from a binary large object (BLOB).
table	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
ctable	casOut	creates the classification table.
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 regression models to a binary large object (BLOB).

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all confidence intervals.

Default	0.05
Range	(0, 1)

applyRowOrder=true | false

when set to True, uses the available groupBy and orderBy information to group and order the data.

Default	false

association=true | false

when sent to True, creates the association table.

Default	false

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

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

binEps=double

specifies the precision of the predicted probabilities that are used for classification.

Default	1E-05
Range	0–1

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

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

For more information about class subparameters, see class Parameter (Shared Concepts).

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

Alias	classVars

classGlobalOpts={classopts}

lists options that apply to all classification variables.

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

classLevelsPrint=true | false

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

Default	true

clb=true | false | "WALD" | "PL"

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

code={aircodegen}

writes SAS DATA step code for computing predicted values of the fitted model

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

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

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

For more information, see Collection Effects (Shared Concepts).

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", ...>}

corrB=true | false

when set to True, displays the correlation matrix of the parameters.

Default	false

covB=true | false

when set to True, displays the covariance matrix of the parameters.

Default	false

ctable={ctableOptions}

creates the classification table.

For more information, see Classification Table and ROC Curves .

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

accuracy="string"

includes and names the accuracy in the classification table.

allStats=true | false

when set to True, requests all available statistics.

Default	false

casOut={casouttable}

specifies the settings for an output table.

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

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

specifies the list of variables to create indexes for in the output data.

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

maxMemSize=64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

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.

replace=true | false

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

Default	false

replication=integer

Default	1
Minimum value	0

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.

threadBlockSize=64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

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

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

specifies cutpoints for the classification table.

fnf="string"

includes and names the false negative fraction in the classification table.

fpf="string"

includes and names the false positive fraction (1-specificity) in the classification table.

lift="string"

includes and names the lift in the classification table.

misclass="string"

includes and names the misclassification rate in the classification table.

nocounts=true | false

when set to True, removes counts from the classification table.

Default	false

npv="string"

includes and names the negative predictive value in the classification table.

pc="string"

includes and names the percent correct in the classification table.

ppv="string"

includes and names the positive predictive value (precision) in the classification table.

tnf="string"

includes and names the true negative fraction (specificity) in the classification table.

tpf="string"

includes and names the true positive fraction (recall, sensitivity) in the classification table.

display={displayTables}

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

For more information about display subparameters, see display Parameter (Shared Concepts).

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

fitData=true | false

when set to True, specifies that the data to be scored were also used to fit the model.

Default	false

freq="variable-name"

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

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

specifies variables to use for analysis.

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

Alias	input

lackfit={lackfitOptions}

creates the Hosmer and Lemeshow tables.

For more information, see The Hosmer-Lemeshow Goodness-of-Fit Test .

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

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

specifies cutpoints for the Hosmer and Lemeshow partitions.

df=double

specifies the degrees of freedom to use for the Hosmer and Lemeshow test.

Minimum value	0

dfReduce=integer

specifies the reduction in degrees of freedom for the Hosmer and Lemeshow test.

Default	2
Minimum value	0

nGroups=integer

specifies the maximum number of groups to create for the Hosmer and Lemeshow test.

Default	10
Minimum value	5

noncentrality=double

specifies the noncentrality parameter for the Hosmer and Lemeshow test.

Default	0
Minimum value	0

powerAdj=true | false

when set to True, adjusts the number of groups so that the Hosmer and Lemeshow test can maintain power.

Default	false

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

specifies the effects and subparameters for least squares means.

For more information, see lsmeans Parameter (Shared Concepts).

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

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

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.

For more information about the methods and the method-specific-parameters, see the description of the adjust subparameter in the lsmeans parameter (Shared Concepts).

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.

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

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

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

displays differences of the least squares means.

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

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

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

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

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

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.

maxOptBatch=64-bit-integer | "AUTO"

controls the number of observations processed in one batch.

For more information, see the description of the pageObs parameter in Memory Usage .

Alias	pageObs

maxResponseLevels=integer

specifies the maximum number of levels allowed for a multinomial response.

Default	100
Minimum value	2

model={logisticModel}

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

For information about model specification, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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

center=true | false

when set to TRUE, centers and scales continuous covariates.

Default	false

centerlasso=true | false

when set to TRUE, centers and scales covariates, including categorical covariates, for the LASSO method.

Default	true

clb=true | false

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

Default	false

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"

dist="BERNOULLI" | "BINOMIAL" | "MULTINOMIAL"

specifies the response distribution for the model.

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

For more information, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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.

entry="variable-name"

specifies the entry variable.

include=integer | {{effect-1} <, {effect-2}, ...>}

The effect value is specified as follows:

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.

informative=true | false

For more information, see Informative Missingness (Shared Concepts).

Default	false

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

link="CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT"

specifies the link function for the model.

For more information, see the LINK= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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.

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

specifies the priors for each response level, which is used for computing the posterior predicted value.

For more information, see the PRIOR= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

ss3=true | false

Default	false

start=integer | {{effect-1} <, {effect-2}, ...>}

The effect value is specified as follows:

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"

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

For more information, see Multimember Effects (Shared Concepts).

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

multipass=true | false

when set to True, levelizes the input data table everytime it is read.

Default	false

nClassLevelsPrint=integer

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

Minimum value	0

noCheck=true | false

when set to True, does not check logistic models for separation.

For more information, see Existence of Maximum Likelihood Estimates .

Default	false

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

specifies nominal variables to use for analysis.

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

Alias	nominal

normalize=true | false

when set to True, divides the log likelihood by the total number of observations during the optimization.

Default	true

nostderr=true | false

when set to True, the covariance matrix and any statistic that depends on it are not computed.

Default	false

noxpx=true | false

when set to True, does not compute X'WX and Hessian matrices, and disables all methods and suppresses all outputs that rely on them.

Default	false

oddsratio={oddsratioOptions}

creates a table that compares subpopulations by using odds ratios.

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

alpha=double

specifies the significance level of the confidence limits.

Default	0.05
Range	(0, 1)

at={{orAtOpts-1} <, {orAtOpts-2}, ...>}

changes the default fixed values or levels for covariates that interact with the odds ratio variable.

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

level="ALL" | "REF" | "string" | {"string-1" <, "string-2", ...>}

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

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

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* var="variable-name"

specifies a covariate that interacts with the odds ratio variable.

cl="PL" | "WALD"

specifies which types of confidence intervals to compute.

Default	WALD

PL

computes profile-likelihood confidence limits.

WALD

computes Wald confidence limits.

diff="ALL" | "REF"

specifies which pairs of response levels to compare.

Default	REF

ALL

specifies all levels of the classification variable.

REF

specifies the reference level of the classification variable.

unit={{orUnitOpts-1} <, {orUnitOpts-2}, ...>}

changes the default units of change for continuous odds ratio variables.

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

stderr=true | false

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	false

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

specifies units of change for a continuous odds ratio variable.

* var="variable-name"

specifies a continuous odds ratio variable.

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

specifies variables for which odds ratios are computed.

Aliases	oddsratios
Aliases	oddsratio

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

at={{orSpecAt-1} <, {orSpecAt-2}, ...>}

specifies fixed values or levels for covariates that interact with the odds ratio variable.

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

level="ALL" | "REF" | "string" | {"string-1" <, "string-2", ...>}

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

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

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* var="variable-name"

specifies a covariate that interacts with the odds ratio variable.

stderr=true | false

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	false

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

specifies units of change for a continuous odds ratio variable.

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

specifies variables for which odds ratios are computed.

optimization={optimizationStatement}

specifies the technique and options for performing the optimization.

For more information, see the description of the parameters in Optimization Parameters (Shared Concepts).

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	absTol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

absXConv=double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

corrections=integer

specifies the number of corrections used in the LBFGS update.

Alias	correction
Default	20
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

inParmEst={castable}

specifies the input initial parameter estimates data table that contains starting values for the optimization.

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

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.

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.

itHist="NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

singRes=double

specifies the singularity criterion for the residual variance.

Range	0–1

technique="CONGRA" | "DBLDOG" | "DUQUANEW" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "TRUREG"

specifies the optimization technique.

For more information, see Choosing an Optimization Algorithm (Shared Concepts).

Alias	tech
Default	NRRIDG

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

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 at the starting parameter values.

NRRIDG

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

TRUREG

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

xConv=double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output={logisticOutputStatement}

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

For more information about the available statistics, see Predicted Probabilities and Regression Diagnostics .

For more information, see OUTPUT Statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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

alpha=double

specifies the significance level to use for the construction of confidence intervals. By default, this is set to the global significance level.

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).

cBar="string"

names the confidence interval displacement, which measures the overall change in the global regression estimates that can be attributed to deleting the individual observation.

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

difChisq="string"

names the change in the Pearson chi-square statistic that can be attributed to deleting the individual observation.

difDev="string"

names the change in the deviance that can be attributed to deleting the individual observation.

h="string"

names the leverage of the observation.

Alias	hatDiag

into="string"

names the predicted response level.

intoCutpt=double

specifies the predicted event probability that determines the predicted binary response level.

Default	0.5

ipred="string"

names the individual predicted value for a cumulative link. If you do not specify any output statistics, then by default the predicted value is named _IPRED_.

Aliases	ip
Aliases	individual

lcl="string"

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

Aliases	lowerXBeta
Aliases	lowerLinP

lclm="string"

names the lower bound of a confidence interval for the mean.

Aliases	lower
Aliases	lowerMean

level="string"

names the ordered response level.

obscat=true | false

when set to True, computes multinomial output statistics at the observed response level.

Default	false

post="string"

names the posterior predicted value.

pred="string"

names the predicted value. If you do not specify any output statistics, then by default the predicted value is named _PRED_.

Aliases	p
	predicted
	iLink
	mean

predprobs=true | false

when set to True, displays requested multinomial predicted probabilities as separate variables.

Default	false

resChi="string"

names the Pearson chi-square residual.

Aliases	pearson
Aliases	pears

resDev="string"

names the deviance residual.

Alias	devResid

resLik="string"

names the likelihood residual (likelihood displacement).

Aliases	likeDist
	ld
	resLike

resRaw="string"

names the raw residual.

Aliases	r
	resid
	residual
	rawResid

resWork="string"

names the working residual.

role="string"

identifies the training, validation, and test roles for the observations.

stdResChi="string"

names the standardized Pearson chi-square residual.

Aliases	adjPearson
Aliases	adjPears

stdResDev="string"

names the standardized deviance residual.

Alias	stdDevResid

stdXBeta="string"

names the standard error of the linear predictor.

Alias	stdP

ucl="string"

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

Aliases	upperXBeta
Aliases	upperLinP

uclm="string"

names the upper bound of a confidence interval for the mean.

Aliases	upper
Aliases	upperMean

xBeta="string"

names the linear predictor.

Alias	linP

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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED"

specifies whether to add raw and formatted values of classification variables in the ParameterEstimates table.

Default	RAW

partByFrac={partByFracStatement}

specifies the fractions of the data to be used for validation and testing.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

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

seed=integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

test=double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

validate=double

Alias	valid
Range	0–1

partByVar={partByVarStatement}

names the variable and its values used to partition the data into training, validation, and testing roles.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

Long form	partByVar={name="variable-name"}
Shortcut form	partByVar="variable-name"

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

* name="variable-name"

names the variable in the input table whose values are used to assign roles to each observation.

test="string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

train="string"

validate="string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

partFit=true | false

when set to True, displays the fit statistics that are produced when your data are partitioned.

For more information, see Partition Fit Statistics .

Default	false

plConv=double

specifies the convergence criterion for the profile likelihood computations.

Default	0.0001
Range	0–1

plMaxIter=integer

specifies the maximum number of iterations for the profile likelihood computations.

Default	25
Minimum value	0

plSingular=double

specifies the tolerance for testing singularity for profile likelihood computations.

Range	0–1

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

For more information, see Polynomial Effects (Shared Concepts).

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

Alias	poly

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

specifies the options for repeated measures analysis.

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

converge=double

specifies the convergence criterion for repeated measures analysis.

Default	0.0001
Minimum value	0

corrb=true | false

when set to True, displays the estimated model-based and empirical correlation matrices of the parameters.

Default	false

corrtype="AR" | "EXCH" | "IND" | "MDEP" | "UN"

specifies the type of correlation structure.

Alias	covtype
Default	IN

AR

specifies the first-order autoregressive correlation structure.

EXCH

specifies the compound symmetry correlation structure.

Alias	CS

IND

specifies the independence correlation structure.

Alias	IN

MDEP

specifies the m-dependent correlation structure.

UN

specifies the unstructured correlation structure.

corrw=true | false

when set to True, displays the estimated working correlation matrix.

Default	false

covb=true | false

when set to True, displays the estimated model-based and empirical covariance matrices of the parameters.

Default	false

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"

ecorrb=true | false

when set to True, displays the estimated empirical correlation matrix of the parameters.

Default	false

ecovb=true | false

when set to True, displays the estimated empirical covariance matrix of the parameters.

Default	false

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.

maxIter=64-bit-integer

specifies the maximum number of iterations for repeated measures analysis.

Default	50
Minimum value	0

mcorrb=true | false

when set to True, displays the estimated model-based correlation matrix of the parameters.

Default	false

mcovb=true | false

when set to True, displays the estimated model-based covariance matrix of the parameters.

Default	false

mdepm=64-bit-integer

specifies the order of the m-dependent correlation structure.

Default	1
Minimum value	1

modelse=true | false

produces a parameter estimates table that displays and uses the model-based standard errors.

Default	false

printmle=true | false

produces the parameter estimates table from the initial stage of estimation.

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"

restore={castable}

restores regression models from a binary large object (BLOB).

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

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.

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

* name="table-name"

specifies the name of the input table.

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.

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

selection={selectionStatement}

specifies the method and options for performing model selection.

For more information, see selection Parameter (Shared Concepts).

Long form	selection={method="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"

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

candidates=integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

choose="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

For more information, see the discussion of the choose subparameter (Shared Concepts).

details="ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

For more information, see the description of the details subparameter (Shared Concepts).

Default	SUMMARY

elasticNetOptions={enOptions}

specifies options to use in performing elastic net selection methods.

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

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	abstol
Default	1E-08
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

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

specifies the regularization parameters in the elastic net selection method.

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

specifies the elastic net mixing parameter.

numLambda=integer

specifies the number of regularization parameters in the elastic net selection method.

Alias	nLambda
Default	0
Minimum value	0

rho=double

specifies the scaling factor to use in computing minimum regularization parameter.

Range	(0, 1)

solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

fast=true | false

Default	false

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

For more information, see the description of the hierarchy subparameter (Shared Concepts).

Default	DEFAULT

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

specifies the coefficients in the relaxed LASSO method.

maxEffects=integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

maxSteps=integer

specifies the maximum number of selection steps to perform.

method="BACKWARD" | "ELASTICNET" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE"

specifies the model selection method.

For more information, see Model Selection Methods (Shared Concepts).

Default	STEPWISE

minEffects=integer

specifies the minimum number of effects in any model to consider during backward selection.

orderSelect=true | false

when set to True, shows effects in parameter estimates tables in the order in which they were added to the model.

Default	false

plots=true | false

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

For more information about the plots you can create, see Model Selection Plots (Shared Concepts in SAS Visual Statistics: Procedures).

Default	false

relaxed=true | false

when set to True, applies the relaxed LASSO method.

Default	false

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

For more information, see the discussion of the select subparameter (Shared Concepts).

slEntry=double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

slStay=double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

stop="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

For more information, see the discussion of the stop subparameter (Shared Concepts).

stopHorizon=integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

For more information, see the description of the stopHorizon subparameter (Shared Concepts).

Default	3

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

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

For more information, see Spline Effects (Shared Concepts).

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

ss3=true | false

Default	false

stb=true | false

Default	false

store={casouttable}

stores regression models to a binary large object (BLOB).

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.

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

specifies text to store that gets displayed when you restore the model.

Alias	storenote

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).

target="string"

specifies the target variable to use for analysis.

useLastIter=true | false

when equal to 1, displays all tables even if there is an optimization error.

Default	false

weight="variable-name"

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

weightNorm=true | false

adjusts the weights so the total weight equals the total frequency.

Default	false

logistic Action

Fits logistic regression models.

Python Syntax
Summary: Input and Output Tables
Parameter Descriptions

Python Syntax

results=s.regression.logistic(

alpha=double,

applyRowOrder=True | False,

association=True | False,

attributes=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

binEps=double,

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",

"split":True | False,

"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",

"split":True | False

classLevelsPrint=True | False,

clb=True | False | "WALD" | "PL",

code={

"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"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

"comment":True | False,

"fmtWdth":integer,

"indentSize":integer,

"intoCutPt":double,

"iProb":True | False,

"labelId":integer,

"lineSize":integer,

"noTrim":True | False,

"pCatAll":True | False,

"tabForm":True | False

collection=[{

"details":True | False,

"name":"string",

"vars":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>],

corrB=True | False,

covB=True | False,

ctable={

"accuracy":"string",

"allStats":True | False,

"casOut":{

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

"name":"table-name"

"replace":True | False

"replication":integer

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

"cutpt":double | [double-1 <, double-2, ...>],

"fnf":"string",

"fpf":"string",

"lift":"string",

"misclass":"string",

"nocounts":True | False,

"npv":"string",

"pc":"string",

"ppv":"string",

"tnf":"string",

"tpf":"string"

display={

"caseSensitive":True | False,

"exclude":True | False,

"excludeAll":True | False,

"keyIsPath":True | False,

"names":["string-1" <, "string-2", ...>],

"pathType":"LABEL" | "NAME",

"traceNames":True | False

fitData=True | False,

freq="variable-name",

inputs=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

lackfit={

"cutpt":double | [double-1 <, double-2, ...>],

"df":double,

"dfReduce":integer,

"nGroups":integer,

"noncentrality":double,

"powerAdj":True | False

lsmeans=[{

"statements":[{

"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,

"e":True | False,

"singular":double,

"terms":[{effect-1} <, {effect-2}, ...>] | ["string-1" <, "string-2", ...>]

}<, {...}>]

}<, {...}>],

maxOptBatch=64-bit-integer | "AUTO",

maxResponseLevels=integer,

model={

"center":True | False,

"centerlasso":True | False,

"clb":True | False,

"depVars":[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"dist":"BERNOULLI" | "BINOMIAL" | "MULTINOMIAL",

"effects":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"entry":"variable-name",

"include":integer | [{effect-1} <, {effect-2}, ...>],

"informative":True | False,

"lassoRho":double,

"lassoSteps":integer,

"lassoTol":double,

"link":"CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT",

"noint":True | False,

"offset":"variable-name",

"prior":double | [double-1 <, double-2, ...>],

"ss3":True | False,

"start":integer | [{effect-1} <, {effect-2}, ...>],

"trial":"variable-name"

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", ...>]

}<, {...}>],

multipass=True | False,

nClassLevelsPrint=integer,

noCheck=True | False,

nominals=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

normalize=True | False,

nostderr=True | False,

noxpx=True | False,

oddsratio={

"alpha":double,

"at":[{

"level":"ALL" | "REF" | "string" | ["string-1" <, "string-2", ...>],

"value":double | [double-1 <, double-2, ...>],

"var":"variable-name"

}<, {...}>],

"cl":"PL" | "WALD",

"diff":"ALL" | "REF",

"unit":[{

"stderr":True | False,

"value":double | [double-1 <, double-2, ...>],

"var":"variable-name"

}<, {...}>],

"vars":[{

"at":[{orSpecAt-1} <, {orSpecAt-2}, ...>],

"stderr":True | False,

"unit":double | [double-1 <, double-2, ...>],

"var":["variable-name-1" <, "variable-name-2", ...>]

}<, {...}>]

optimization={

"absConv":double,

"absFConv":double,

"absGConv":double,

"absXConv":double,

"corrections":integer,

"fConv":double,

"fConv2":double,

"gConv":double,

"gConv2":double,

"inParmEst":{

"caslib":"string"

"computedOnDemand":True | False

"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"

"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"

}

"itHist":"NONE" | "SUMMARY",

"maxFunc":double,

"maxIter":double,

"maxTime":double,

"minIter":integer,

"singRes":double,

"xConv":double

output={

"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", ...>]

"cBar":"string",

"copyVars":"ALL" | "ALL_MODEL" | "ALL_NUMERIC" | ["variable-name-1" <, "variable-name-2", ...>],

"difChisq":"string",

"difDev":"string",

"h":"string",

"into":"string",

"intoCutpt":double,

"ipred":"string",

"lcl":"string",

"lclm":"string",

"level":"string",

"obscat":True | False,

"post":"string",

"pred":"string",

"predprobs":True | False,

"resChi":"string",

"resDev":"string",

"resLik":"string",

"resRaw":"string",

"resWork":"string",

"role":"string",

"stdResChi":"string",

"stdResDev":"string",

"stdXBeta":"string",

"ucl":"string",

"uclm":"string",

"xBeta":"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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED",

partByFrac={

"seed":integer,

"test":double,

"validate":double

partByVar={

"name":"variable-name",

"test":"string",

"train":"string",

"validate":"string"

partFit=True | False,

plConv=double,

plMaxIter=integer,

plSingular=double,

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", ...>]

}<, {...}>],

repeated=[{

"converge":double,

"corrb":True | False,

"corrtype":"AR" | "EXCH" | "IND" | "MDEP" | "UN",

"corrw":True | False,

"covb":True | False,

"depVars":[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"ecorrb":True | False,

"ecovb":True | False,

"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", ...>]

}<, {...}>],

"maxIter":64-bit-integer,

"mcorrb":True | False,

"mcovb":True | False,

"mdepm":64-bit-integer,

"modelse":True | False,

"printmle":True | False,

"subject":[{

"interaction":"BAR" | "CROSS" | "NONE",

"maxInteract":integer,

"nest":["string-1" <, "string-2", ...>],

"vars":["string-1" <, "string-2", ...>]

}<, {...}>],

"trial":"variable-name"

}<, {...}>],

restore={

"caslib":"string",

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

"name":"table-name",

"whereTable":{

"casLib":"string"

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

seed=64-bit-integer,

selection={

"candidates":integer | "ALL",

"details":"ALL" | "NONE" | "STEPS" | "SUMMARY",

"elasticNetOptions":{

"absFConv":double

"fConv":double

"gConv":double

"lambda_":[double-1 <, double-2, ...>]

"mixing":[double-1 <, double-2, ...>]

"numLambda":integer

"rho":double

"solver":"ADMM" | "BFGS" | "LBFGS" | "NLP"

"fast":True | False,

"hierarchy":"DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

"kappa":[double-1 <, double-2, ...>],

"maxEffects":integer,

"maxSteps":integer,

"minEffects":integer,

"orderSelect":True | False,

"plots":True | False,

"relaxed":True | False,

"select":"AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

"slEntry":double,

"slStay":double,

"stopHorizon":integer

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", ...>]

}<, {...}>],

ss3=True | False,

stb=True | False,

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"

storetext=["string-1" <, "string-2", ...>],

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"

}

target="string",

useLastIter=True | False,

weight="variable-name",

weightNorm=True | False

)

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
optimization	inParmEst	specifies the technique and options for performing the optimization.
restore	—	restores regression models from a binary large object (BLOB).
table	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
ctable	casOut	creates the classification table.
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 regression models to a binary large object (BLOB).

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all confidence intervals.

Default	0.05
Range	(0, 1)

applyRowOrder=True | False

when set to True, uses the available groupBy and orderBy information to group and order the data.

Default	False

association=True | False

when sent to True, creates the association table.

Default	False

attributes=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

binEps=double

specifies the precision of the predicted probabilities that are used for classification.

Default	1E-05
Range	0–1

class_=[{classStatement-1} <, {classStatement-2}, ...>]

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

For more information about class subparameters, see class Parameter (Shared Concepts).

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

Alias	classVars

classGlobalOpts={classopts}

lists options that apply to all classification variables.

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

classLevelsPrint=True | False

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

Default	True

clb=True | False | "WALD" | "PL"

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

code={aircodegen}

writes SAS DATA step code for computing predicted values of the fitted model

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

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

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

For more information, see Collection Effects (Shared Concepts).

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", ...>]

corrB=True | False

when set to True, displays the correlation matrix of the parameters.

Default	False

covB=True | False

when set to True, displays the covariance matrix of the parameters.

Default	False

ctable={ctableOptions}

creates the classification table.

For more information, see Classification Table and ROC Curves .

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

"accuracy":"string"

includes and names the accuracy in the classification table.

"allStats":True | False

when set to True, requests all available statistics.

Default	False

"casOut":{casouttable}

specifies the settings for an output table.

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

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

specifies the list of variables to create indexes for in the output data.

"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

"maxMemSize":64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

"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.

"replace":True | False

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

Default	False

"replication":integer

Default	1
Minimum value	0

"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.

"threadBlockSize":64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

"timeStamp":"string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

"where":["string-1" <, "string-2", ...>]

"cutpt":double | [double-1 <, double-2, ...>]

specifies cutpoints for the classification table.

"fnf":"string"

includes and names the false negative fraction in the classification table.

"fpf":"string"

includes and names the false positive fraction (1-specificity) in the classification table.

"lift":"string"

includes and names the lift in the classification table.

"misclass":"string"

includes and names the misclassification rate in the classification table.

"nocounts":True | False

when set to True, removes counts from the classification table.

Default	False

"npv":"string"

includes and names the negative predictive value in the classification table.

"pc":"string"

includes and names the percent correct in the classification table.

"ppv":"string"

includes and names the positive predictive value (precision) in the classification table.

"tnf":"string"

includes and names the true negative fraction (specificity) in the classification table.

"tpf":"string"

includes and names the true positive fraction (recall, sensitivity) in the classification table.

display={displayTables}

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

For more information about display subparameters, see display Parameter (Shared Concepts).

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

fitData=True | False

when set to True, specifies that the data to be scored were also used to fit the model.

Default	False

freq="variable-name"

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

inputs=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies variables to use for analysis.

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

Alias	input

lackfit={lackfitOptions}

creates the Hosmer and Lemeshow tables.

For more information, see The Hosmer-Lemeshow Goodness-of-Fit Test .

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

"cutpt":double | [double-1 <, double-2, ...>]

specifies cutpoints for the Hosmer and Lemeshow partitions.

"df":double

specifies the degrees of freedom to use for the Hosmer and Lemeshow test.

Minimum value	0

"dfReduce":integer

specifies the reduction in degrees of freedom for the Hosmer and Lemeshow test.

Default	2
Minimum value	0

"nGroups":integer

specifies the maximum number of groups to create for the Hosmer and Lemeshow test.

Default	10
Minimum value	5

"noncentrality":double

specifies the noncentrality parameter for the Hosmer and Lemeshow test.

Default	0
Minimum value	0

"powerAdj":True | False

when set to True, adjusts the number of groups so that the Hosmer and Lemeshow test can maintain power.

Default	False

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

specifies the effects and subparameters for least squares means.

For more information, see lsmeans Parameter (Shared Concepts).

* "statements":[{lsmeansList-1} <, {lsmeansList-2}, ...>]

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

"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.

For more information about the methods and the method-specific-parameters, see the description of the adjust subparameter in the lsmeans parameter (Shared Concepts).

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.

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

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

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

displays differences of the least squares means.

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

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

* "terms":[{effect-1} <, {effect-2}, ...>] | ["string-1" <, "string-2", ...>]

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

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

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.

maxOptBatch=64-bit-integer | "AUTO"

controls the number of observations processed in one batch.

For more information, see the description of the pageObs parameter in Memory Usage .

Alias	pageObs

maxResponseLevels=integer

specifies the maximum number of levels allowed for a multinomial response.

Default	100
Minimum value	2

model={logisticModel}

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

For information about model specification, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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

"center":True | False

when set to TRUE, centers and scales continuous covariates.

Default	False

"centerlasso":True | False

when set to TRUE, centers and scales covariates, including categorical covariates, for the LASSO method.

Default	True

"clb":True | False

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

Default	False

"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"

"dist":"BERNOULLI" | "BINOMIAL" | "MULTINOMIAL"

specifies the response distribution for the model.

"effects":[{effect-1} <, {effect-2}, ...>]

For more information, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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.

"entry":"variable-name"

specifies the entry variable.

"include":integer | [{effect-1} <, {effect-2}, ...>]

The effect value is specified as follows:

"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.

"informative":True | False

For more information, see Informative Missingness (Shared Concepts).

Default	False

"lassoRho":double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

"lassoSteps":integer

specifies the maximum number of steps for the LASSO method.

Default	20

"lassoTol":double

specifies the convergence criterion for the LASSO method.

Default	1E-06

"link":"CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT"

specifies the link function for the model.

For more information, see the LINK= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

"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.

"prior":double | [double-1 <, double-2, ...>]

specifies the priors for each response level, which is used for computing the posterior predicted value.

For more information, see the PRIOR= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

"ss3":True | False

Default	False

"start":integer | [{effect-1} <, {effect-2}, ...>]

The effect value is specified as follows:

"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"

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

For more information, see Multimember Effects (Shared Concepts).

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

multipass=True | False

when set to True, levelizes the input data table everytime it is read.

Default	False

nClassLevelsPrint=integer

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

Minimum value	0

noCheck=True | False

when set to True, does not check logistic models for separation.

For more information, see Existence of Maximum Likelihood Estimates .

Default	False

nominals=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies nominal variables to use for analysis.

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

Alias	nominal

normalize=True | False

when set to True, divides the log likelihood by the total number of observations during the optimization.

Default	True

nostderr=True | False

when set to True, the covariance matrix and any statistic that depends on it are not computed.

Default	False

noxpx=True | False

when set to True, does not compute X'WX and Hessian matrices, and disables all methods and suppresses all outputs that rely on them.

Default	False

oddsratio={oddsratioOptions}

creates a table that compares subpopulations by using odds ratios.

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

"alpha":double

specifies the significance level of the confidence limits.

Default	0.05
Range	(0, 1)

"at":[{orAtOpts-1} <, {orAtOpts-2}, ...>]

changes the default fixed values or levels for covariates that interact with the odds ratio variable.

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

"level":"ALL" | "REF" | "string" | ["string-1" <, "string-2", ...>]

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

"value":double | [double-1 <, double-2, ...>]

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* "var":"variable-name"

specifies a covariate that interacts with the odds ratio variable.

"cl":"PL" | "WALD"

specifies which types of confidence intervals to compute.

Default	WALD

PL

computes profile-likelihood confidence limits.

WALD

computes Wald confidence limits.

"diff":"ALL" | "REF"

specifies which pairs of response levels to compare.

Default	REF

ALL

specifies all levels of the classification variable.

REF

specifies the reference level of the classification variable.

"unit":[{orUnitOpts-1} <, {orUnitOpts-2}, ...>]

changes the default units of change for continuous odds ratio variables.

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

"stderr":True | False

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	False

"value":double | [double-1 <, double-2, ...>]

specifies units of change for a continuous odds ratio variable.

* "var":"variable-name"

specifies a continuous odds ratio variable.

"vars":[{orSpec-1} <, {orSpec-2}, ...>]

specifies variables for which odds ratios are computed.

Aliases	oddsratios
Aliases	oddsratio

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

"at":[{orSpecAt-1} <, {orSpecAt-2}, ...>]

specifies fixed values or levels for covariates that interact with the odds ratio variable.

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

"level":"ALL" | "REF" | "string" | ["string-1" <, "string-2", ...>]

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

"value":double | [double-1 <, double-2, ...>]

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* "var":"variable-name"

specifies a covariate that interacts with the odds ratio variable.

"stderr":True | False

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	False

"unit":double | [double-1 <, double-2, ...>]

specifies units of change for a continuous odds ratio variable.

* "var":["variable-name-1" <, "variable-name-2", ...>]

specifies variables for which odds ratios are computed.

optimization={optimizationStatement}

specifies the technique and options for performing the optimization.

For more information, see the description of the parameters in Optimization Parameters (Shared Concepts).

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

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

"absConv":double

specifies the absolute function convergence criterion.

Alias	absTol

"absFConv":double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

"absGConv":double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

"absXConv":double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

"corrections":integer

specifies the number of corrections used in the LBFGS update.

Alias	correction
Default	20
Minimum value	0

"fConv":double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

"fConv2":double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

"gConv":double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

"gConv2":double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

"inParmEst":{castable}

specifies the input initial parameter estimates data table that contains starting values for the optimization.

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

"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.

"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.

"itHist":"NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

"maxFunc":double

specifies the maximum number of function evaluations.

Minimum value	0

"maxIter":double

specifies the maximum number of iterations.

Minimum value	0

"maxTime":double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

"minIter":integer

specifies the minimum number of iterations.

Minimum value	0

"singRes":double

specifies the singularity criterion for the residual variance.

Range	0–1

"technique":"CONGRA" | "DBLDOG" | "DUQUANEW" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "TRUREG"

specifies the optimization technique.

For more information, see Choosing an Optimization Algorithm (Shared Concepts).

Alias	tech
Default	NRRIDG

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

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 at the starting parameter values.

NRRIDG

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

TRUREG

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

"xConv":double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output={logisticOutputStatement}

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

For more information about the available statistics, see Predicted Probabilities and Regression Diagnostics .

For more information, see OUTPUT Statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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

"alpha":double

specifies the significance level to use for the construction of confidence intervals. By default, this is set to the global significance level.

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).

"cBar":"string"

names the confidence interval displacement, which measures the overall change in the global regression estimates that can be attributed to deleting the individual observation.

"copyVars":"ALL" | "ALL_MODEL" | "ALL_NUMERIC" | ["variable-name-1" <, "variable-name-2", ...>]

"difChisq":"string"

names the change in the Pearson chi-square statistic that can be attributed to deleting the individual observation.

"difDev":"string"

names the change in the deviance that can be attributed to deleting the individual observation.

"h":"string"

names the leverage of the observation.

Alias	hatDiag

"into":"string"

names the predicted response level.

"intoCutpt":double

specifies the predicted event probability that determines the predicted binary response level.

Default	0.5

"ipred":"string"

names the individual predicted value for a cumulative link. If you do not specify any output statistics, then by default the predicted value is named _IPRED_.

Aliases	ip
Aliases	individual

"lcl":"string"

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

Aliases	lowerXBeta
Aliases	lowerLinP

"lclm":"string"

names the lower bound of a confidence interval for the mean.

Aliases	lower
Aliases	lowerMean

"level":"string"

names the ordered response level.

"obscat":True | False

when set to True, computes multinomial output statistics at the observed response level.

Default	False

"post":"string"

names the posterior predicted value.

"pred":"string"

names the predicted value. If you do not specify any output statistics, then by default the predicted value is named _PRED_.

Aliases	p
	predicted
	iLink
	mean

"predprobs":True | False

when set to True, displays requested multinomial predicted probabilities as separate variables.

Default	False

"resChi":"string"

names the Pearson chi-square residual.

Aliases	pearson
Aliases	pears

"resDev":"string"

names the deviance residual.

Alias	devResid

"resLik":"string"

names the likelihood residual (likelihood displacement).

Aliases	likeDist
	ld
	resLike

"resRaw":"string"

names the raw residual.

Aliases	r
	resid
	residual
	rawResid

"resWork":"string"

names the working residual.

"role":"string"

identifies the training, validation, and test roles for the observations.

"stdResChi":"string"

names the standardized Pearson chi-square residual.

Aliases	adjPearson
Aliases	adjPears

"stdResDev":"string"

names the standardized deviance residual.

Alias	stdDevResid

"stdXBeta":"string"

names the standard error of the linear predictor.

Alias	stdP

"ucl":"string"

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

Aliases	upperXBeta
Aliases	upperLinP

"uclm":"string"

names the upper bound of a confidence interval for the mean.

Aliases	upper
Aliases	upperMean

"xBeta":"string"

names the linear predictor.

Alias	linP

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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED"

specifies whether to add raw and formatted values of classification variables in the ParameterEstimates table.

Default	RAW

partByFrac={partByFracStatement}

specifies the fractions of the data to be used for validation and testing.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

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

"seed":integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

"test":double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

"validate":double

Alias	valid
Range	0–1

partByVar={partByVarStatement}

names the variable and its values used to partition the data into training, validation, and testing roles.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

Long form	partByVar={"name":"variable-name"}
Shortcut form	partByVar="variable-name"

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

* "name":"variable-name"

names the variable in the input table whose values are used to assign roles to each observation.

"test":"string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

"train":"string"

"validate":"string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

partFit=True | False

when set to True, displays the fit statistics that are produced when your data are partitioned.

For more information, see Partition Fit Statistics .

Default	False

plConv=double

specifies the convergence criterion for the profile likelihood computations.

Default	0.0001
Range	0–1

plMaxIter=integer

specifies the maximum number of iterations for the profile likelihood computations.

Default	25
Minimum value	0

plSingular=double

specifies the tolerance for testing singularity for profile likelihood computations.

Range	0–1

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

For more information, see Polynomial Effects (Shared Concepts).

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

Alias	poly

repeated=[{logisticModelRepeated-1} <, {logisticModelRepeated-2}, ...>]

specifies the options for repeated measures analysis.

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

"converge":double

specifies the convergence criterion for repeated measures analysis.

Default	0.0001
Minimum value	0

"corrb":True | False

when set to True, displays the estimated model-based and empirical correlation matrices of the parameters.

Default	False

"corrtype":"AR" | "EXCH" | "IND" | "MDEP" | "UN"

specifies the type of correlation structure.

Alias	covtype
Default	IN

AR

specifies the first-order autoregressive correlation structure.

EXCH

specifies the compound symmetry correlation structure.

Alias	CS

IND

specifies the independence correlation structure.

Alias	IN

MDEP

specifies the m-dependent correlation structure.

UN

specifies the unstructured correlation structure.

"corrw":True | False

when set to True, displays the estimated working correlation matrix.

Default	False

"covb":True | False

when set to True, displays the estimated model-based and empirical covariance matrices of the parameters.

Default	False

"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"

"ecorrb":True | False

when set to True, displays the estimated empirical correlation matrix of the parameters.

Default	False

"ecovb":True | False

when set to True, displays the estimated empirical covariance matrix of the parameters.

Default	False

"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.

"maxIter":64-bit-integer

specifies the maximum number of iterations for repeated measures analysis.

Default	50
Minimum value	0

"mcorrb":True | False

when set to True, displays the estimated model-based correlation matrix of the parameters.

Default	False

"mcovb":True | False

when set to True, displays the estimated model-based covariance matrix of the parameters.

Default	False

"mdepm":64-bit-integer

specifies the order of the m-dependent correlation structure.

Default	1
Minimum value	1

"modelse":True | False

produces a parameter estimates table that displays and uses the model-based standard errors.

Default	False

"printmle":True | False

produces the parameter estimates table from the initial stage of estimation.

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"

restore={castable}

restores regression models from a binary large object (BLOB).

Long form	restore={"name":"table-name"}
Shortcut form	restore="table-name"

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.

"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

* "name":"table-name"

specifies the name of the input table.

"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.

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

selection={selectionStatement}

specifies the method and options for performing model selection.

For more information, see selection Parameter (Shared Concepts).

Long form	selection={"method":"BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"

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

"candidates":integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

"choose":"AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

For more information, see the discussion of the choose subparameter (Shared Concepts).

"details":"ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

For more information, see the description of the details subparameter (Shared Concepts).

Default	SUMMARY

"elasticNetOptions":{enOptions}

specifies options to use in performing elastic net selection methods.

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

"absFConv":double

specifies the absolute function difference convergence criterion.

Alias	abstol
Default	1E-08
Minimum value	0

"fConv":double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

"gConv":double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

"lambda_":[double-1 <, double-2, ...>]

specifies the regularization parameters in the elastic net selection method.

"mixing":[double-1 <, double-2, ...>]

specifies the elastic net mixing parameter.

"numLambda":integer

specifies the number of regularization parameters in the elastic net selection method.

Alias	nLambda
Default	0
Minimum value	0

"rho":double

specifies the scaling factor to use in computing minimum regularization parameter.

Range	(0, 1)

"solver":"ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

"fast":True | False

Default	False

"hierarchy":"DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

For more information, see the description of the hierarchy subparameter (Shared Concepts).

Default	DEFAULT

"kappa":[double-1 <, double-2, ...>]

specifies the coefficients in the relaxed LASSO method.

"maxEffects":integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

"maxSteps":integer

specifies the maximum number of selection steps to perform.

"method":"BACKWARD" | "ELASTICNET" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE"

specifies the model selection method.

For more information, see Model Selection Methods (Shared Concepts).

Default	STEPWISE

"minEffects":integer

specifies the minimum number of effects in any model to consider during backward selection.

"orderSelect":True | False

when set to True, shows effects in parameter estimates tables in the order in which they were added to the model.

Default	False

"plots":True | False

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

For more information about the plots you can create, see Model Selection Plots (Shared Concepts in SAS Visual Statistics: Procedures).

Default	False

"relaxed":True | False

when set to True, applies the relaxed LASSO method.

Default	False

"select":"AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

For more information, see the discussion of the select subparameter (Shared Concepts).

"slEntry":double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

"slStay":double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

"stop":"AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

For more information, see the discussion of the stop subparameter (Shared Concepts).

"stopHorizon":integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

For more information, see the description of the stopHorizon subparameter (Shared Concepts).

Default	3

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

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

For more information, see Spline Effects (Shared Concepts).

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

ss3=True | False

Default	False

stb=True | False

Default	False

store={casouttable}

stores regression models to a binary large object (BLOB).

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.

storetext=["string-1" <, "string-2", ...>]

specifies text to store that gets displayed when you restore the model.

Alias	storenote

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).

target="string"

specifies the target variable to use for analysis.

useLastIter=True | False

when equal to 1, displays all tables even if there is an optimization error.

Default	False

weight="variable-name"

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

weightNorm=True | False

adjusts the weights so the total weight equals the total frequency.

Default	False

logistic Action

Fits logistic regression models.

R Syntax
Summary: Input and Output Tables
Parameter Descriptions

R Syntax

results <– cas.regression.logistic(s,

alpha=double,

applyRowOrder=TRUE | FALSE,

association=TRUE | FALSE,

attributes=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

binEps=double,

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",

split=TRUE | FALSE,

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",

split=TRUE | FALSE

classLevelsPrint=TRUE | FALSE,

clb=TRUE | FALSE | "WALD" | "PL",

code=list(

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"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

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

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

comment=TRUE | FALSE,

fmtWdth=integer,

indentSize=integer,

intoCutPt=double,

iProb=TRUE | FALSE,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

pCatAll=TRUE | FALSE,

tabForm=TRUE | FALSE

collection=list( list(

details=TRUE | FALSE,

name="string",

vars=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>),

corrB=TRUE | FALSE,

covB=TRUE | FALSE,

ctable=list(

accuracy="string",

allStats=TRUE | FALSE,

casOut=list(

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"

replace=TRUE | FALSE

replication=integer

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

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

cutpt=double | list(double-1 <, double-2, ...>),

fnf="string",

fpf="string",

lift="string",

misclass="string",

nocounts=TRUE | FALSE,

npv="string",

pc="string",

ppv="string",

tnf="string",

tpf="string"

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

fitData=TRUE | FALSE,

freq="variable-name",

inputs=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

lackfit=list(

cutpt=double | list(double-1 <, double-2, ...>),

df=double,

dfReduce=integer,

nGroups=integer,

noncentrality=double,

powerAdj=TRUE | FALSE

lsmeans=list( list(

statements=list( list(

alpha=double,

at="MEANS" | list(lsmeansOptionAt),

cl=TRUE | FALSE,

controlLevel=list("string-1" <, "string-2", ...>),

corr=TRUE | FALSE,

cov=TRUE | FALSE,

e=TRUE | FALSE,

singular=double,

terms=list( list(effect-1) <, list(effect-2), ...>) | list("string-1" <, "string-2", ...>)

) <, list(...)>)

) <, list(...)>),

maxOptBatch=64-bit-integer | "AUTO",

maxResponseLevels=integer,

model=list(

center=TRUE | FALSE,

centerlasso=TRUE | FALSE,

clb=TRUE | FALSE,

depVars=list( list(

name="variable-name",

options=list(modelopts)

) <, list(...)>),

dist="BERNOULLI" | "BINOMIAL" | "MULTINOMIAL",

effects=list( list(

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

maxInteract=integer,

nest=list("string-1" <, "string-2", ...>),

vars=list("string-1" <, "string-2", ...>)

) <, list(...)>),

entry="variable-name",

include=integer | list( list(effect-1) <, list(effect-2), ...>),

informative=TRUE | FALSE,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

link="CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT",

noint=TRUE | FALSE,

offset="variable-name",

prior=double | list(double-1 <, double-2, ...>),

ss3=TRUE | FALSE,

start=integer | list( list(effect-1) <, list(effect-2), ...>),

trial="variable-name"

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(...)>),

multipass=TRUE | FALSE,

nClassLevelsPrint=integer,

noCheck=TRUE | FALSE,

nominals=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

normalize=TRUE | FALSE,

nostderr=TRUE | FALSE,

noxpx=TRUE | FALSE,

oddsratio=list(

alpha=double,

at=list( list(

level="ALL" | "REF" | "string" | list("string-1" <, "string-2", ...>),

value=double | list(double-1 <, double-2, ...>),

var="variable-name"

) <, list(...)>),

cl="PL" | "WALD",

diff="ALL" | "REF",

unit=list( list(

stderr=TRUE | FALSE,

value=double | list(double-1 <, double-2, ...>),

var="variable-name"

) <, list(...)>),

vars=list( list(

at=list( list(orSpecAt-1) <, list(orSpecAt-2), ...>),

stderr=TRUE | FALSE,

unit=double | list(double-1 <, double-2, ...>),

var=list("variable-name-1" <, "variable-name-2", ...>)

) <, list(...)>)

optimization=list(

absConv=double,

absFConv=double,

absGConv=double,

absXConv=double,

corrections=integer,

fConv=double,

fConv2=double,

gConv=double,

gConv2=double,

inParmEst=list(

caslib="string"

computedOnDemand=TRUE | FALSE

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"

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"

)

itHist="NONE" | "SUMMARY",

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

singRes=double,

xConv=double

output=list(

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", ...>)

cBar="string",

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("variable-name-1" <, "variable-name-2", ...>),

difChisq="string",

difDev="string",

h="string",

into="string",

intoCutpt=double,

ipred="string",

lcl="string",

lclm="string",

level="string",

obscat=TRUE | FALSE,

post="string",

pred="string",

predprobs=TRUE | FALSE,

resChi="string",

resDev="string",

resLik="string",

resRaw="string",

resWork="string",

role="string",

stdResChi="string",

stdResDev="string",

stdXBeta="string",

ucl="string",

uclm="string",

xBeta="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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED",

partByFrac=list(

seed=integer,

test=double,

validate=double

partByVar=list(

name="variable-name",

test="string",

train="string",

validate="string"

partFit=TRUE | FALSE,

plConv=double,

plMaxIter=integer,

plSingular=double,

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(...)>),

repeated=list( list(

converge=double,

corrb=TRUE | FALSE,

corrtype="AR" | "EXCH" | "IND" | "MDEP" | "UN",

corrw=TRUE | FALSE,

covb=TRUE | FALSE,

depVars=list( list(

name="variable-name",

options=list(modelopts)

) <, list(...)>),

ecorrb=TRUE | FALSE,

ecovb=TRUE | FALSE,

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(...)>),

maxIter=64-bit-integer,

mcorrb=TRUE | FALSE,

mcovb=TRUE | FALSE,

mdepm=64-bit-integer,

modelse=TRUE | FALSE,

printmle=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(...)>),

restore=list(

caslib="string",

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),

name="table-name",

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"

)

seed=64-bit-integer,

selection=list(

candidates=integer | "ALL",

details="ALL" | "NONE" | "STEPS" | "SUMMARY",

elasticNetOptions=list(

absFConv=double

fConv=double

gConv=double

lambda=list(double-1 <, double-2, ...>)

mixing=list(double-1 <, double-2, ...>)

numLambda=integer

rho=double

solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

fast=TRUE | FALSE,

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

kappa=list(double-1 <, double-2, ...>),

maxEffects=integer,

maxSteps=integer,

minEffects=integer,

orderSelect=TRUE | FALSE,

plots=TRUE | FALSE,

relaxed=TRUE | FALSE,

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

slEntry=double,

slStay=double,

stopHorizon=integer

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(...)>),

ss3=TRUE | FALSE,

stb=TRUE | FALSE,

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"

storetext=list("string-1" <, "string-2", ...>),

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"

)

target="string",

useLastIter=TRUE | FALSE,

weight="variable-name",

weightNorm=TRUE | FALSE

)

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
optimization	inParmEst	specifies the technique and options for performing the optimization.
restore	—	restores regression models from a binary large object (BLOB).
table	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
ctable	casOut	creates the classification table.
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 regression models to a binary large object (BLOB).

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all confidence intervals.

Default	0.05
Range	(0, 1)

applyRowOrder=TRUE | FALSE

when set to True, uses the available groupBy and orderBy information to group and order the data.

Default	FALSE

association=TRUE | FALSE

when sent to True, creates the association table.

Default	FALSE

attributes=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

binEps=double

specifies the precision of the predicted probabilities that are used for classification.

Default	1E-05
Range	0–1

class=list( list(classStatement-1) <, list(classStatement-2), ...>)

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

For more information about class subparameters, see class Parameter (Shared Concepts).

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

Alias	classVars

classGlobalOpts=list(classopts)

lists options that apply to all classification variables.

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

classLevelsPrint=TRUE | FALSE

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

Default	TRUE

clb=TRUE | FALSE | "WALD" | "PL"

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

code=list(aircodegen)

writes SAS DATA step code for computing predicted values of the fitted model

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

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.

For more information, see Collection Effects (Shared Concepts).

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", ...>)

corrB=TRUE | FALSE

when set to True, displays the correlation matrix of the parameters.

Default	FALSE

covB=TRUE | FALSE

when set to True, displays the covariance matrix of the parameters.

Default	FALSE

ctable=list(ctableOptions)

creates the classification table.

For more information, see Classification Table and ROC Curves .

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

accuracy="string"

includes and names the accuracy in the classification table.

allStats=TRUE | FALSE

when set to True, requests all available statistics.

Default	FALSE

casOut=list(casouttable)

specifies the settings for an output table.

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

indexVars=list("variable-name-1" <, "variable-name-2", ...>)

specifies the list of variables to create indexes for in the output data.

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

maxMemSize=64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

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.

replace=TRUE | FALSE

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

Default	FALSE

replication=integer

Default	1
Minimum value	0

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.

threadBlockSize=64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

where=list("string-1" <, "string-2", ...>)

cutpt=double | list(double-1 <, double-2, ...>)

specifies cutpoints for the classification table.

fnf="string"

includes and names the false negative fraction in the classification table.

fpf="string"

includes and names the false positive fraction (1-specificity) in the classification table.

lift="string"

includes and names the lift in the classification table.

misclass="string"

includes and names the misclassification rate in the classification table.

nocounts=TRUE | FALSE

when set to True, removes counts from the classification table.

Default	FALSE

npv="string"

includes and names the negative predictive value in the classification table.

pc="string"

includes and names the percent correct in the classification table.

ppv="string"

includes and names the positive predictive value (precision) in the classification table.

tnf="string"

includes and names the true negative fraction (specificity) in the classification table.

tpf="string"

includes and names the true positive fraction (recall, sensitivity) in the classification table.

display=list(displayTables)

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

For more information about display subparameters, see display Parameter (Shared Concepts).

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

fitData=TRUE | FALSE

when set to True, specifies that the data to be scored were also used to fit the model.

Default	FALSE

freq="variable-name"

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

inputs=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies variables to use for analysis.

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

Alias	input

lackfit=list(lackfitOptions)

creates the Hosmer and Lemeshow tables.

For more information, see The Hosmer-Lemeshow Goodness-of-Fit Test .

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

cutpt=double | list(double-1 <, double-2, ...>)

specifies cutpoints for the Hosmer and Lemeshow partitions.

df=double

specifies the degrees of freedom to use for the Hosmer and Lemeshow test.

Minimum value	0

dfReduce=integer

specifies the reduction in degrees of freedom for the Hosmer and Lemeshow test.

Default	2
Minimum value	0

nGroups=integer

specifies the maximum number of groups to create for the Hosmer and Lemeshow test.

Default	10
Minimum value	5

noncentrality=double

specifies the noncentrality parameter for the Hosmer and Lemeshow test.

Default	0
Minimum value	0

powerAdj=TRUE | FALSE

when set to True, adjusts the number of groups so that the Hosmer and Lemeshow test can maintain power.

Default	FALSE

lsmeans=list( list(lsmeansStatement-1) <, list(lsmeansStatement-2), ...>)

specifies the effects and subparameters for least squares means.

For more information, see lsmeans Parameter (Shared Concepts).

* statements=list( list(lsmeansList-1) <, list(lsmeansList-2), ...>)

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

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.

For more information about the methods and the method-specific-parameters, see the description of the adjust subparameter in the lsmeans parameter (Shared Concepts).

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.

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

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

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

displays differences of the least squares means.

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

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

* 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.

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

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.

maxOptBatch=64-bit-integer | "AUTO"

controls the number of observations processed in one batch.

For more information, see the description of the pageObs parameter in Memory Usage .

Alias	pageObs

maxResponseLevels=integer

specifies the maximum number of levels allowed for a multinomial response.

Default	100
Minimum value	2

model=list(logisticModel)

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

For information about model specification, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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

center=TRUE | FALSE

when set to TRUE, centers and scales continuous covariates.

Default	FALSE

centerlasso=TRUE | FALSE

when set to TRUE, centers and scales covariates, including categorical covariates, for the LASSO method.

Default	TRUE

clb=TRUE | FALSE

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

Default	FALSE

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"

dist="BERNOULLI" | "BINOMIAL" | "MULTINOMIAL"

specifies the response distribution for the model.

effects=list( list(effect-1) <, list(effect-2), ...>)

For more information, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions).

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.

entry="variable-name"

specifies the entry variable.

include=integer | list( list(effect-1) <, list(effect-2), ...>)

The effect value is specified as follows:

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.

informative=TRUE | FALSE

For more information, see Informative Missingness (Shared Concepts).

Default	FALSE

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

link="CLOGLOG" | "GLOGIT" | "LOGIT" | "LOGLOG" | "NORMIT"

specifies the link function for the model.

For more information, see the LINK= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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.

prior=double | list(double-1 <, double-2, ...>)

specifies the priors for each response level, which is used for computing the posterior predicted value.

For more information, see the PRIOR= option in the MODEL statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

ss3=TRUE | FALSE

Default	FALSE

start=integer | list( list(effect-1) <, list(effect-2), ...>)

The effect value is specified as follows:

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"

multimember=list( list(multimember-1) <, list(multimember-2), ...>)

For more information, see Multimember Effects (Shared Concepts).

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

multipass=TRUE | FALSE

when set to True, levelizes the input data table everytime it is read.

Default	FALSE

nClassLevelsPrint=integer

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

Minimum value	0

noCheck=TRUE | FALSE

when set to True, does not check logistic models for separation.

For more information, see Existence of Maximum Likelihood Estimates .

Default	FALSE

nominals=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies nominal variables to use for analysis.

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

Alias	nominal

normalize=TRUE | FALSE

when set to True, divides the log likelihood by the total number of observations during the optimization.

Default	TRUE

nostderr=TRUE | FALSE

when set to True, the covariance matrix and any statistic that depends on it are not computed.

Default	FALSE

noxpx=TRUE | FALSE

when set to True, does not compute X'WX and Hessian matrices, and disables all methods and suppresses all outputs that rely on them.

Default	FALSE

oddsratio=list(oddsratioOptions)

creates a table that compares subpopulations by using odds ratios.

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

alpha=double

specifies the significance level of the confidence limits.

Default	0.05
Range	(0, 1)

at=list( list(orAtOpts-1) <, list(orAtOpts-2), ...>)

changes the default fixed values or levels for covariates that interact with the odds ratio variable.

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

level="ALL" | "REF" | "string" | list("string-1" <, "string-2", ...>)

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

value=double | list(double-1 <, double-2, ...>)

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* var="variable-name"

specifies a covariate that interacts with the odds ratio variable.

cl="PL" | "WALD"

specifies which types of confidence intervals to compute.

Default	WALD

PL

computes profile-likelihood confidence limits.

WALD

computes Wald confidence limits.

diff="ALL" | "REF"

specifies which pairs of response levels to compare.

Default	REF

ALL

specifies all levels of the classification variable.

REF

specifies the reference level of the classification variable.

unit=list( list(orUnitOpts-1) <, list(orUnitOpts-2), ...>)

changes the default units of change for continuous odds ratio variables.

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

stderr=TRUE | FALSE

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	FALSE

value=double | list(double-1 <, double-2, ...>)

specifies units of change for a continuous odds ratio variable.

* var="variable-name"

specifies a continuous odds ratio variable.

vars=list( list(orSpec-1) <, list(orSpec-2), ...>)

specifies variables for which odds ratios are computed.

Aliases	oddsratios
Aliases	oddsratio

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

at=list( list(orSpecAt-1) <, list(orSpecAt-2), ...>)

specifies fixed values or levels for covariates that interact with the odds ratio variable.

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

level="ALL" | "REF" | "string" | list("string-1" <, "string-2", ...>)

specifies fixed levels for a classification covariate that interacts with the odds ratio variable.

Default	REF
ALL	specifies all levels of the classification variable.
REF	specifies the reference level of the classification variable.

value=double | list(double-1 <, double-2, ...>)

specifies fixed values for a continuous covariate that interacts with the odds ratio variable.

* var="variable-name"

specifies a covariate that interacts with the odds ratio variable.

stderr=TRUE | FALSE

when set to True, specifies that the units of change for a continuous odds ratio variable are multiples of the standard error.

Default	FALSE

unit=double | list(double-1 <, double-2, ...>)

specifies units of change for a continuous odds ratio variable.

* var=list("variable-name-1" <, "variable-name-2", ...>)

specifies variables for which odds ratios are computed.

optimization=list(optimizationStatement)

specifies the technique and options for performing the optimization.

For more information, see the description of the parameters in Optimization Parameters (Shared Concepts).

Long form	optimization=list(technique="CONGRA" \| "DBLDOG" \| "DUQUANEW" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG")
Shortcut form	optimization="CONGRA" \| "DBLDOG" \| "DUQUANEW" \| "LBFGS" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"

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

absConv=double

specifies the absolute function convergence criterion.

Alias	absTol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

absXConv=double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

corrections=integer

specifies the number of corrections used in the LBFGS update.

Alias	correction
Default	20
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

inParmEst=list(castable)

specifies the input initial parameter estimates data table that contains starting values for the optimization.

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

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.

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.

itHist="NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

singRes=double

specifies the singularity criterion for the residual variance.

Range	0–1

technique="CONGRA" | "DBLDOG" | "DUQUANEW" | "LBFGS" | "NEWRAP" | "NMSIMP" | "NONE" | "NRRIDG" | "TRUREG"

specifies the optimization technique.

For more information, see Choosing an Optimization Algorithm (Shared Concepts).

Alias	tech
Default	NRRIDG

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

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 at the starting parameter values.

NRRIDG

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

TRUREG

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

xConv=double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output=list(logisticOutputStatement)

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

For more information about the available statistics, see Predicted Probabilities and Regression Diagnostics .

For more information, see OUTPUT Statement (LOGSELECT Procedure in SAS Visual Statistics: Procedures).

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

alpha=double

specifies the significance level to use for the construction of confidence intervals. By default, this is set to the global significance level.

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).

cBar="string"

names the confidence interval displacement, which measures the overall change in the global regression estimates that can be attributed to deleting the individual observation.

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("variable-name-1" <, "variable-name-2", ...>)

difChisq="string"

names the change in the Pearson chi-square statistic that can be attributed to deleting the individual observation.

difDev="string"

names the change in the deviance that can be attributed to deleting the individual observation.

h="string"

names the leverage of the observation.

Alias	hatDiag

into="string"

names the predicted response level.

intoCutpt=double

specifies the predicted event probability that determines the predicted binary response level.

Default	0.5

ipred="string"

names the individual predicted value for a cumulative link. If you do not specify any output statistics, then by default the predicted value is named _IPRED_.

Aliases	ip
Aliases	individual

lcl="string"

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

Aliases	lowerXBeta
Aliases	lowerLinP

lclm="string"

names the lower bound of a confidence interval for the mean.

Aliases	lower
Aliases	lowerMean

level="string"

names the ordered response level.

obscat=TRUE | FALSE

when set to True, computes multinomial output statistics at the observed response level.

Default	FALSE

post="string"

names the posterior predicted value.

pred="string"

names the predicted value. If you do not specify any output statistics, then by default the predicted value is named _PRED_.

Aliases	p
	predicted
	iLink
	mean

predprobs=TRUE | FALSE

when set to True, displays requested multinomial predicted probabilities as separate variables.

Default	FALSE

resChi="string"

names the Pearson chi-square residual.

Aliases	pearson
Aliases	pears

resDev="string"

names the deviance residual.

Alias	devResid

resLik="string"

names the likelihood residual (likelihood displacement).

Aliases	likeDist
	ld
	resLike

resRaw="string"

names the raw residual.

Aliases	r
	resid
	residual
	rawResid

resWork="string"

names the working residual.

role="string"

identifies the training, validation, and test roles for the observations.

stdResChi="string"

names the standardized Pearson chi-square residual.

Aliases	adjPearson
Aliases	adjPears

stdResDev="string"

names the standardized deviance residual.

Alias	stdDevResid

stdXBeta="string"

names the standard error of the linear predictor.

Alias	stdP

ucl="string"

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

Aliases	upperXBeta
Aliases	upperLinP

uclm="string"

names the upper bound of a confidence interval for the mean.

Aliases	upper
Aliases	upperMean

xBeta="string"

names the linear predictor.

Alias	linP

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

parmEstLevDetails="NONE" | "RAW" | "RAW_AND_FORMATTED"

specifies whether to add raw and formatted values of classification variables in the ParameterEstimates table.

Default	RAW

partByFrac=list(partByFracStatement)

specifies the fractions of the data to be used for validation and testing.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

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

seed=integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

test=double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

validate=double

Alias	valid
Range	0–1

partByVar=list(partByVarStatement)

names the variable and its values used to partition the data into training, validation, and testing roles.

For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts).

Long form	partByVar=list(name="variable-name")
Shortcut form	partByVar="variable-name"

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

* name="variable-name"

names the variable in the input table whose values are used to assign roles to each observation.

test="string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

train="string"

validate="string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

partFit=TRUE | FALSE

when set to True, displays the fit statistics that are produced when your data are partitioned.

For more information, see Partition Fit Statistics .

Default	FALSE

plConv=double

specifies the convergence criterion for the profile likelihood computations.

Default	0.0001
Range	0–1

plMaxIter=integer

specifies the maximum number of iterations for the profile likelihood computations.

Default	25
Minimum value	0

plSingular=double

specifies the tolerance for testing singularity for profile likelihood computations.

Range	0–1

polynomial=list( list(polynomial-1) <, list(polynomial-2), ...>)

For more information, see Polynomial Effects (Shared Concepts).

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

Alias	poly

repeated=list( list(logisticModelRepeated-1) <, list(logisticModelRepeated-2), ...>)

specifies the options for repeated measures analysis.

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

converge=double

specifies the convergence criterion for repeated measures analysis.

Default	0.0001
Minimum value	0

corrb=TRUE | FALSE

when set to True, displays the estimated model-based and empirical correlation matrices of the parameters.

Default	FALSE

corrtype="AR" | "EXCH" | "IND" | "MDEP" | "UN"

specifies the type of correlation structure.

Alias	covtype
Default	IN

AR

specifies the first-order autoregressive correlation structure.

EXCH

specifies the compound symmetry correlation structure.

Alias	CS

IND

specifies the independence correlation structure.

Alias	IN

MDEP

specifies the m-dependent correlation structure.

UN

specifies the unstructured correlation structure.

corrw=TRUE | FALSE

when set to True, displays the estimated working correlation matrix.

Default	FALSE

covb=TRUE | FALSE

when set to True, displays the estimated model-based and empirical covariance matrices of the parameters.

Default	FALSE

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"

ecorrb=TRUE | FALSE

when set to True, displays the estimated empirical correlation matrix of the parameters.

Default	FALSE

ecovb=TRUE | FALSE

when set to True, displays the estimated empirical covariance matrix of the parameters.

Default	FALSE

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.

maxIter=64-bit-integer

specifies the maximum number of iterations for repeated measures analysis.

Default	50
Minimum value	0

mcorrb=TRUE | FALSE

when set to True, displays the estimated model-based correlation matrix of the parameters.

Default	FALSE

mcovb=TRUE | FALSE

when set to True, displays the estimated model-based covariance matrix of the parameters.

Default	FALSE

mdepm=64-bit-integer

specifies the order of the m-dependent correlation structure.

Default	1
Minimum value	1

modelse=TRUE | FALSE

produces a parameter estimates table that displays and uses the model-based standard errors.

Default	FALSE

printmle=TRUE | FALSE

produces the parameter estimates table from the initial stage of estimation.

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"

restore=list(castable)

restores regression models from a binary large object (BLOB).

Long form	restore=list(name="table-name")
Shortcut form	restore="table-name"

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.

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

* name="table-name"

specifies the name of the input table.

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.

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

selection=list(selectionStatement)

specifies the method and options for performing model selection.

For more information, see selection Parameter (Shared Concepts).

Long form	selection=list(method="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE")
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"

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

candidates=integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

choose="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

For more information, see the discussion of the choose subparameter (Shared Concepts).

details="ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

For more information, see the description of the details subparameter (Shared Concepts).

Default	SUMMARY

elasticNetOptions=list(enOptions)

specifies options to use in performing elastic net selection methods.

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

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	abstol
Default	1E-08
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

lambda=list(double-1 <, double-2, ...>)

specifies the regularization parameters in the elastic net selection method.

mixing=list(double-1 <, double-2, ...>)

specifies the elastic net mixing parameter.

numLambda=integer

specifies the number of regularization parameters in the elastic net selection method.

Alias	nLambda
Default	0
Minimum value	0

rho=double

specifies the scaling factor to use in computing minimum regularization parameter.

Range	(0, 1)

solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

fast=TRUE | FALSE

Default	FALSE

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

For more information, see the description of the hierarchy subparameter (Shared Concepts).

Default	DEFAULT

kappa=list(double-1 <, double-2, ...>)

specifies the coefficients in the relaxed LASSO method.

maxEffects=integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

maxSteps=integer

specifies the maximum number of selection steps to perform.

method="BACKWARD" | "ELASTICNET" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE"

specifies the model selection method.

For more information, see Model Selection Methods (Shared Concepts).

Default	STEPWISE

minEffects=integer

specifies the minimum number of effects in any model to consider during backward selection.

orderSelect=TRUE | FALSE

when set to True, shows effects in parameter estimates tables in the order in which they were added to the model.

Default	FALSE

plots=TRUE | FALSE

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

For more information about the plots you can create, see Model Selection Plots (Shared Concepts in SAS Visual Statistics: Procedures).

Default	FALSE

relaxed=TRUE | FALSE

when set to True, applies the relaxed LASSO method.

Default	FALSE

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

For more information, see the discussion of the select subparameter (Shared Concepts).

slEntry=double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

slStay=double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

stop="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

For more information, see the discussion of the stop subparameter (Shared Concepts).

stopHorizon=integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

For more information, see the description of the stopHorizon subparameter (Shared Concepts).

Default	3

spline=list( list(spline-1) <, list(spline-2), ...>)

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

For more information, see Spline Effects (Shared Concepts).

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

ss3=TRUE | FALSE

Default	FALSE

stb=TRUE | FALSE

Default	FALSE

store=list(casouttable)

stores regression models to a binary large object (BLOB).

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.

storetext=list("string-1" <, "string-2", ...>)

specifies text to store that gets displayed when you restore the model.

Alias	storenote

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).

target="string"

specifies the target variable to use for analysis.

useLastIter=TRUE | FALSE

when equal to 1, displays all tables even if there is an optimization error.

Default	FALSE

weight="variable-name"

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

weightNorm=TRUE | FALSE

adjusts the weights so the total weight equals the total frequency.

Default	FALSE

Last updated: March 05, 2026