Partial Least Squares Action Set

Provides actions for fitting reduced-rank linear models, including partial least squares

pls Action

Fits models by using any one of a number of linear predictive methods, including partial least squares.

CASL Syntax

pls.pls <result=results> <status=rc> /
attributes={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
cenScale=TRUE | FALSE,
class={{
countMissing=TRUE | FALSE,
descending=TRUE | FALSE,
ignoreMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
classGlobalOptions={
countMissing=TRUE | FALSE,
descending=TRUE | FALSE,
ignoreMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE
},
classLevelsPrint=TRUE | FALSE,
collection={{
details=TRUE | FALSE,
required parameter name="string",
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
cvTest={
nSamp=integer,
pValue=double,
seed=integer,
stat="PRESS" | "T2"
},
details=TRUE | FALSE,
display={
caseSensitive=TRUE | FALSE,
exclude=TRUE | FALSE,
excludeAll=TRUE | FALSE,
keyIsPath=TRUE | FALSE,
names={"string-1" <, "string-2", ...>},
pathType="LABEL" | "NAME",
traceNames=TRUE | FALSE
},
groupbyLimit=64-bit-integer,
inputs={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
required parameter method={
algorithm="EIG" | "NIPALS" | "SVD",
epsilon=double,
maxIter=integer,
required parameter name="PCR" | "PLS" | "RRR" | "SIMPLS"
},
model={
depVars={{
name="variable-name"
}, {...}},
effects={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
intercept=TRUE | FALSE,
solution=TRUE | FALSE
},
multimember={{
details=TRUE | FALSE,
required parameter name="string",
noEffect=TRUE | FALSE,
stdize=TRUE | FALSE,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>},
weight={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
nClassLevelsPrint=integer,
nFactors=integer,
noCenter=TRUE | FALSE,
noCVStdize=TRUE | FALSE,
nominals={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
noScale=TRUE | FALSE,
output={
required parameter casOut={
caslib="string"
compress=TRUE | FALSE
indexVars={"variable-name-1" <, "variable-name-2", ...>}
label="string"
lifetime=64-bit-integer
maxMemSize=64-bit-integer
memoryFormat="DVR" | "INHERIT" | "STANDARD"
name="table-name"
promote=TRUE | FALSE
replace=TRUE | FALSE
replication=integer
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where={"string-1" <, "string-2", ...>}
},
copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>},
h="string",
predicted="string",
press="string",
role="string",
t2="string",
xResidual="string",
xScore="string",
xStd="string",
xStdsse="string",
yResidual="string",
yScore="string",
yStd="string",
yStdsse="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
},
partitionByFrac={
seed=integer,
test=double
},
partitionByVar={
required parameter name="variable-name",
test="string",
train="string"
},
polynomial={{
degree=integer,
details=TRUE | FALSE,
labelStyle={
expand=TRUE | FALSE
exponent="string"
includeName=TRUE | FALSE
productSymbol="NONE" | "string"
},
mDegree=integer,
required parameter name="string",
noSeparate=TRUE | FALSE,
standardize={
method="MOMENTS" | "MRANGE" | "WMOMENTS"
options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
prefix="NONE" | "string"
},
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
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,
required parameter name="string",
naturalCubic=TRUE | FALSE,
separate=TRUE | FALSE,
split=TRUE | FALSE,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
required parameter table={
caslib="string",
computedOnDemand=TRUE | FALSE,
computedVars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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",
required parameter 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},
required parameter name="table-name",
orderBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
singlePass=TRUE | FALSE,
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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}
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}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}}
where="where-expression"
}
},
target="string",
varss=TRUE | FALSE
;
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

specifies the settings for an input table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 output

required parametercasOut

creates a data 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.

Parameter Descriptions

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

cenScale=TRUE | FALSE

when set to True, displays the centering and scaling information.

Default FALSE

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

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

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

Alias classVars

classGlobalOptions={classopts}

specifies options that apply to all classification variables.

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

Alias classGlobalOpts

classLevelsPrint=TRUE | FALSE

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

Default TRUE

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

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

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

details=TRUE | FALSE

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

Default FALSE
* name="string"

specifies the name of the effect.

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

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

cvTest={cvTestOptions}

performs van der Voet's randomization-based model comparison test.

Long form cvTest={stat="PRESS" | "T2"}
Shortcut form cvTest="PRESS" | "T2"

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

nSamp=integer

specifies the number of randomizations to perform.

Default 1000
Range 0–MACINT
pValue=double

specifies the cutoff probability for declaring an insignificant difference.

Alias pVal
Default 0.1
Range 0–1
seed=integer

specifies the seed value for the random number stream.

Default 0
Minimum value 0
stat="PRESS" | "T2"

specifies the test statistic for the model comparison. You can specify either T2, for Hotelling's T^2 statistic, or PRESS, for the predicted residual sum of squares.

Default T2

details=TRUE | FALSE

when set to True, displays the details of the fitted model.

Alias detail
Default FALSE

display={displayTables}

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

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

groupbyLimit=64-bit-integer

suppresses analysis if the number of BY groups exceeds the specified value.

Minimum value 1

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

* method={methodOptions}

specifies the settings for the general factor extraction method.

Long form method={name="PCR" | "PLS" | "RRR" | "SIMPLS"}
Shortcut form method="PCR" | "PLS" | "RRR" | "SIMPLS"

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

algorithm="EIG" | "NIPALS" | "SVD"

specifies the algorithm used to compute extracted PLS factors.

Alias alg
Default NIPALS
epsilon=double

specifies the convergence criterion for the NIPALS algorithm.

Alias eps
Default 1E-12
Range 0–1
maxIter=integer

specifies the maximum number of iterations for the NIPALS algorithm.

Default 200
Range 0–MACINT
* name="PCR" | "PLS" | "RRR" | "SIMPLS"

specifies the name of the general factor extraction method to use.

model={modelOptions}

specifies the responses and the predictors, which determine the Y and X matrices of the model, respectively.

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

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
target
name="variable-name"

names the response variable.

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

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

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

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

specifies the type of interaction for the variables.

Alias interact
Default NONE
maxInteract=integer

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

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

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.

intercept=TRUE | FALSE

when set to True, includes the intercept term in the model.

Default FALSE
solution=TRUE | FALSE

when set to True, displays the coefficients of the final predictive model for the responses.

Default FALSE

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

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

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

nClassLevelsPrint=integer

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

Minimum value 0

nFactors=integer

specifies the number of factors to extract.

Aliases nFac
lv
Default 0
Minimum value 0

noCenter=TRUE | FALSE

when set to True, suppresses centering of the responses and predictors before fitting.

Default FALSE

noCVStdize=TRUE | FALSE

when set to True, suppresses re-centering and rescaling of the responses and predictors when cross validating.

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

noScale=TRUE | FALSE

when set to True, suppresses scaling of the responses and predictors before fitting.

Default FALSE

output={outputOptions}

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

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

* casOut={casouttable}

specifies the settings for an output table.

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

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

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

h="string"

requests the approximate leverage. If set to an empty string, the prefix H is used for naming the output variable.

predicted="string"

requests predicted values for each response. If set to an empty string, the prefix Pred is used for naming the output variables.

Aliases p
pred
press="string"

requests approximate predicted residuals for each response. If set to an empty string, the prefix PRESS is used for naming the output variables.

role="string"

requests numeric values that indicate the role played by each observation in fitting the model. If set to an empty string, the prefix _ROLE_ is used for naming the output variable.

t2="string"

requests scaled sum of squares of score values. If set to an empty string, the prefix TSquare is used for naming the output variable.

Alias tSquare
xResidual="string"

requests residuals for each predictor. If set to an empty string, the prefix XResid is used for naming the output variables.

Aliases xr
xResid
xScore="string"

requests extracted factors (X-scores, latent vectors, latent variables, T) for each selected model factor. If set to an empty string, the prefix XScore is used for naming the output variables.

xStd="string"

requests standardized (centered and scaled) predictor values for each predictor. If set to an empty string, the prefix StdX is used for naming the output variables.

Alias stdX
xStdsse="string"

requests the sum of squares of residuals for standardized predictors. If set to an empty string, the prefix StdXSSE is used for naming the output variable.

Aliases xQres
stdXsse
yResidual="string"

requests residuals for each response. If set to an empty string, the prefix YResid is used for naming the output variables.

Aliases yr
yResid
yScore="string"

requests extracted responses (Y-scores, U) for each selected model factor. If set to an empty string, the prefix YScore is used for naming the output variables.

yStd="string"

requests standardized (centered and scaled) response values for each response. If set to an empty string, the prefix StdY is used for naming the output variables.

Alias stdY
yStdsse="string"

requests the sum of squares of residuals for standardized responses. If set to an empty string, the prefix StdYSSE is used for naming the output variable.

Aliases yQres
stdYsse

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

partitionByFrac={partByFracStatement}

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

Alias partByFrac

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

partitionByVar={partByVarStatement}

specifies the variable and its values used to partition the data into training and testing roles.

Alias partByVar
Long form partitionByVar={name="variable-name"}
Shortcut form partitionByVar="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.

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

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

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

Alias poly

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

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

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

* table={castable}

specifies the settings for an input 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.

varss=TRUE | FALSE

when set to True, displays the amount of variation accounted for in each response and predictor.

Default FALSE

pls Action

Fits models by using any one of a number of linear predictive methods, including partial least squares.

Lua Syntax

results, info = s:pls_pls{
attributes={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
cenScale=true | false,
class={{
countMissing=true | false,
descending=true | false,
ignoreMissing=true | false,
levelizeRaw=true | false,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=true | false,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
classGlobalOptions={
countMissing=true | false,
descending=true | false,
ignoreMissing=true | false,
levelizeRaw=true | false,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=true | false
},
classLevelsPrint=true | false,
collection={{
details=true | false,
required parameter name="string",
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
cvTest={
nSamp=integer,
pValue=double,
seed=integer,
stat="PRESS" | "T2"
},
details=true | false,
display={
caseSensitive=true | false,
exclude=true | false,
excludeAll=true | false,
keyIsPath=true | false,
names={"string-1" <, "string-2", ...>},
pathType="LABEL" | "NAME",
traceNames=true | false
},
groupbyLimit=64-bit-integer,
inputs={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
required parameter method={
algorithm="EIG" | "NIPALS" | "SVD",
epsilon=double,
maxIter=integer,
required parameter name="PCR" | "PLS" | "RRR" | "SIMPLS"
},
model={
depVars={{
name="variable-name"
}, {...}},
effects={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
intercept=true | false,
solution=true | false
},
multimember={{
details=true | false,
required parameter name="string",
noEffect=true | false,
stdize=true | false,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>},
weight={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
nClassLevelsPrint=integer,
nFactors=integer,
noCenter=true | false,
noCVStdize=true | false,
nominals={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
noScale=true | false,
output={
required parameter casOut={
caslib="string"
compress=true | false
indexVars={"variable-name-1" <, "variable-name-2", ...>}
label="string"
lifetime=64-bit-integer
maxMemSize=64-bit-integer
memoryFormat="DVR" | "INHERIT" | "STANDARD"
name="table-name"
promote=true | false
replace=true | false
replication=integer
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where={"string-1" <, "string-2", ...>}
},
copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>},
h="string",
predicted="string",
press="string",
role="string",
t2="string",
xResidual="string",
xScore="string",
xStd="string",
xStdsse="string",
yResidual="string",
yScore="string",
yStd="string",
yStdsse="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
},
partitionByFrac={
seed=integer,
test=double
},
partitionByVar={
required parameter name="variable-name",
test="string",
train="string"
},
polynomial={{
degree=integer,
details=true | false,
labelStyle={
expand=true | false
exponent="string"
includeName=true | false
productSymbol="NONE" | "string"
},
mDegree=integer,
required parameter name="string",
noSeparate=true | false,
standardize={
method="MOMENTS" | "MRANGE" | "WMOMENTS"
options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
prefix="NONE" | "string"
},
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
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,
required parameter name="string",
naturalCubic=true | false,
separate=true | false,
split=true | false,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
required parameter table={
caslib="string",
computedOnDemand=true | false,
computedVars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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",
required parameter 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},
required parameter name="table-name",
orderBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
singlePass=true | false,
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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}
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}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}}
where="where-expression"
}
},
target="string",
varss=true | false
}
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

specifies the settings for an input table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 output

required parametercasOut

creates a data 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.

Parameter Descriptions

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

cenScale=true | false

when set to True, displays the centering and scaling information.

Default false

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

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

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

Alias classVars

classGlobalOptions={classopts}

specifies options that apply to all classification variables.

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

Alias classGlobalOpts

classLevelsPrint=true | false

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

Default true

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

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

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

details=true | false

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

Default false
* name="string"

specifies the name of the effect.

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

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

cvTest={cvTestOptions}

performs van der Voet's randomization-based model comparison test.

Long form cvTest={stat="PRESS" | "T2"}
Shortcut form cvTest="PRESS" | "T2"

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

nSamp=integer

specifies the number of randomizations to perform.

Default 1000
Range 0–MACINT
pValue=double

specifies the cutoff probability for declaring an insignificant difference.

Alias pVal
Default 0.1
Range 0–1
seed=integer

specifies the seed value for the random number stream.

Default 0
Minimum value 0
stat="PRESS" | "T2"

specifies the test statistic for the model comparison. You can specify either T2, for Hotelling's T^2 statistic, or PRESS, for the predicted residual sum of squares.

Default T2

details=true | false

when set to True, displays the details of the fitted model.

Alias detail
Default false

display={displayTables}

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

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

groupbyLimit=64-bit-integer

suppresses analysis if the number of BY groups exceeds the specified value.

Minimum value 1

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

* method={methodOptions}

specifies the settings for the general factor extraction method.

Long form method={name="PCR" | "PLS" | "RRR" | "SIMPLS"}
Shortcut form method="PCR" | "PLS" | "RRR" | "SIMPLS"

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

algorithm="EIG" | "NIPALS" | "SVD"

specifies the algorithm used to compute extracted PLS factors.

Alias alg
Default NIPALS
epsilon=double

specifies the convergence criterion for the NIPALS algorithm.

Alias eps
Default 1E-12
Range 0–1
maxIter=integer

specifies the maximum number of iterations for the NIPALS algorithm.

Default 200
Range 0–MACINT
* name="PCR" | "PLS" | "RRR" | "SIMPLS"

specifies the name of the general factor extraction method to use.

model={modelOptions}

specifies the responses and the predictors, which determine the Y and X matrices of the model, respectively.

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

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
target
name="variable-name"

names the response variable.

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

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

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

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

specifies the type of interaction for the variables.

Alias interact
Default NONE
maxInteract=integer

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

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

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.

intercept=true | false

when set to True, includes the intercept term in the model.

Default false
solution=true | false

when set to True, displays the coefficients of the final predictive model for the responses.

Default false

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

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

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

nClassLevelsPrint=integer

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

Minimum value 0

nFactors=integer

specifies the number of factors to extract.

Aliases nFac
lv
Default 0
Minimum value 0

noCenter=true | false

when set to True, suppresses centering of the responses and predictors before fitting.

Default false

noCVStdize=true | false

when set to True, suppresses re-centering and rescaling of the responses and predictors when cross validating.

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

noScale=true | false

when set to True, suppresses scaling of the responses and predictors before fitting.

Default false

output={outputOptions}

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

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

* casOut={casouttable}

specifies the settings for an output table.

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

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

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

h="string"

requests the approximate leverage. If set to an empty string, the prefix H is used for naming the output variable.

predicted="string"

requests predicted values for each response. If set to an empty string, the prefix Pred is used for naming the output variables.

Aliases p
pred
press="string"

requests approximate predicted residuals for each response. If set to an empty string, the prefix PRESS is used for naming the output variables.

role="string"

requests numeric values that indicate the role played by each observation in fitting the model. If set to an empty string, the prefix _ROLE_ is used for naming the output variable.

t2="string"

requests scaled sum of squares of score values. If set to an empty string, the prefix TSquare is used for naming the output variable.

Alias tSquare
xResidual="string"

requests residuals for each predictor. If set to an empty string, the prefix XResid is used for naming the output variables.

Aliases xr
xResid
xScore="string"

requests extracted factors (X-scores, latent vectors, latent variables, T) for each selected model factor. If set to an empty string, the prefix XScore is used for naming the output variables.

xStd="string"

requests standardized (centered and scaled) predictor values for each predictor. If set to an empty string, the prefix StdX is used for naming the output variables.

Alias stdX
xStdsse="string"

requests the sum of squares of residuals for standardized predictors. If set to an empty string, the prefix StdXSSE is used for naming the output variable.

Aliases xQres
stdXsse
yResidual="string"

requests residuals for each response. If set to an empty string, the prefix YResid is used for naming the output variables.

Aliases yr
yResid
yScore="string"

requests extracted responses (Y-scores, U) for each selected model factor. If set to an empty string, the prefix YScore is used for naming the output variables.

yStd="string"

requests standardized (centered and scaled) response values for each response. If set to an empty string, the prefix StdY is used for naming the output variables.

Alias stdY
yStdsse="string"

requests the sum of squares of residuals for standardized responses. If set to an empty string, the prefix StdYSSE is used for naming the output variable.

Aliases yQres
stdYsse

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

partitionByFrac={partByFracStatement}

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

Alias partByFrac

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

partitionByVar={partByVarStatement}

specifies the variable and its values used to partition the data into training and testing roles.

Alias partByVar
Long form partitionByVar={name="variable-name"}
Shortcut form partitionByVar="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.

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

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

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

Alias poly

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

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

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

* table={castable}

specifies the settings for an input 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.

varss=true | false

when set to True, displays the amount of variation accounted for in each response and predictor.

Default false

pls Action

Fits models by using any one of a number of linear predictive methods, including partial least squares.

Python Syntax

results=s.pls.pls(
attributes=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
cenScale=True | False,
class_=[{
"countMissing":True | False,
"descending":True | False,
"ignoreMissing":True | False,
"levelizeRaw":True | False,
"maxLev":integer,
"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
"ref":"FIRST" | "LAST" | double | "string",
"split":True | False,
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
classGlobalOptions={
"countMissing":True | False,
"descending":True | False,
"ignoreMissing":True | False,
"levelizeRaw":True | False,
"maxLev":integer,
"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
"ref":"FIRST" | "LAST" | double | "string",
"split":True | False
},
classLevelsPrint=True | False,
collection=[{
"details":True | False,
required parameter "name":"string",
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
cvTest={
"nSamp":integer,
"pValue":double,
"seed":integer,
"stat":"PRESS" | "T2"
},
details=True | False,
display={
"caseSensitive":True | False,
"exclude":True | False,
"excludeAll":True | False,
"keyIsPath":True | False,
"names":["string-1" <, "string-2", ...>],
"pathType":"LABEL" | "NAME",
"traceNames":True | False
},
groupbyLimit=64-bit-integer,
inputs=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
required parameter method={
"algorithm":"EIG" | "NIPALS" | "SVD",
"epsilon":double,
"maxIter":integer,
required parameter "name":"PCR" | "PLS" | "RRR" | "SIMPLS"
},
model={
"depVars":[{
"name":"variable-name"
}<, {...}>],
"effects":[{
"interaction":"BAR" | "CROSS" | "NONE",
"maxInteract":integer,
"nest":["string-1" <, "string-2", ...>],
required parameter "vars":["string-1" <, "string-2", ...>]
}<, {...}>],
"intercept":True | False,
"solution":True | False
},
multimember=[{
"details":True | False,
required parameter "name":"string",
"noEffect":True | False,
"stdize":True | False,
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>],
"weight":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
nClassLevelsPrint=integer,
nFactors=integer,
noCenter=True | False,
noCVStdize=True | False,
nominals=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
noScale=True | False,
output={
required parameter "casOut":{
"caslib":"string"
"compress":True | False
"indexVars":["variable-name-1" <, "variable-name-2", ...>]
"label":"string"
"lifetime":64-bit-integer
"maxMemSize":64-bit-integer
"memoryFormat":"DVR" | "INHERIT" | "STANDARD"
"name":"table-name"
"promote":True | False
"replace":True | False
"replication":integer
"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"
"threadBlockSize":64-bit-integer
"timeStamp":"string"
"where":["string-1" <, "string-2", ...>]
},
"copyVars":"ALL" | "ALL_MODEL" | "ALL_NUMERIC" | ["variable-name-1" <, "variable-name-2", ...>],
"h":"string",
"predicted":"string",
"press":"string",
"role":"string",
"t2":"string",
"xResidual":"string",
"xScore":"string",
"xStd":"string",
"xStdsse":"string",
"yResidual":"string",
"yScore":"string",
"yStd":"string",
"yStdsse":"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
},
partitionByFrac={
"seed":integer,
"test":double
},
partitionByVar={
required parameter "name":"variable-name",
"test":"string",
"train":"string"
},
polynomial=[{
"degree":integer,
"details":True | False,
"labelStyle":{
"expand":True | False
"exponent":"string"
"includeName":True | False
"productSymbol":"NONE" | "string"
},
"mDegree":integer,
required parameter "name":"string",
"noSeparate":True | False,
"standardize":{
"method":"MOMENTS" | "MRANGE" | "WMOMENTS"
"options":"CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
"prefix":"NONE" | "string"
},
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
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,
required parameter "name":"string",
"naturalCubic":True | False,
"separate":True | False,
"split":True | False,
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
required parameter table={
"caslib":"string",
"computedOnDemand":True | False,
"computedVars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "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",
required parameter "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},
required parameter "name":"table-name",
"orderBy":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
"singlePass":True | False,
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "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}
"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}
required parameter "name":"table-name"
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>]
"where":"where-expression"
}
},
target="string",
varss=True | False
)
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

specifies the settings for an input table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 output

required parametercasOut

creates a data 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.

Parameter Descriptions

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

cenScale=True | False

when set to True, displays the centering and scaling information.

Default False

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

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

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

Alias classVars

classGlobalOptions={classopts}

specifies options that apply to all classification variables.

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

Alias classGlobalOpts

classLevelsPrint=True | False

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

Default True

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

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

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

"details":True | False

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

Default False
* "name":"string"

specifies the name of the effect.

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

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

cvTest={cvTestOptions}

performs van der Voet's randomization-based model comparison test.

Long form cvTest={"stat":"PRESS" | "T2"}
Shortcut form cvTest="PRESS" | "T2"

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

"nSamp":integer

specifies the number of randomizations to perform.

Default 1000
Range 0–MACINT
"pValue":double

specifies the cutoff probability for declaring an insignificant difference.

Alias pVal
Default 0.1
Range 0–1
"seed":integer

specifies the seed value for the random number stream.

Default 0
Minimum value 0
"stat":"PRESS" | "T2"

specifies the test statistic for the model comparison. You can specify either T2, for Hotelling's T^2 statistic, or PRESS, for the predicted residual sum of squares.

Default T2

details=True | False

when set to True, displays the details of the fitted model.

Alias detail
Default False

display={displayTables}

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

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

groupbyLimit=64-bit-integer

suppresses analysis if the number of BY groups exceeds the specified value.

Minimum value 1

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

* method={methodOptions}

specifies the settings for the general factor extraction method.

Long form method={"name":"PCR" | "PLS" | "RRR" | "SIMPLS"}
Shortcut form method="PCR" | "PLS" | "RRR" | "SIMPLS"

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

"algorithm":"EIG" | "NIPALS" | "SVD"

specifies the algorithm used to compute extracted PLS factors.

Alias alg
Default NIPALS
"epsilon":double

specifies the convergence criterion for the NIPALS algorithm.

Alias eps
Default 1E-12
Range 0–1
"maxIter":integer

specifies the maximum number of iterations for the NIPALS algorithm.

Default 200
Range 0–MACINT
* "name":"PCR" | "PLS" | "RRR" | "SIMPLS"

specifies the name of the general factor extraction method to use.

model={modelOptions}

specifies the responses and the predictors, which determine the Y and X matrices of the model, respectively.

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

"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
target
"name":"variable-name"

names the response variable.

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

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

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

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

specifies the type of interaction for the variables.

Alias interact
Default NONE
"maxInteract":integer

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

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

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.

"intercept":True | False

when set to True, includes the intercept term in the model.

Default False
"solution":True | False

when set to True, displays the coefficients of the final predictive model for the responses.

Default False

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

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

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

nClassLevelsPrint=integer

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

Minimum value 0

nFactors=integer

specifies the number of factors to extract.

Aliases nFac
lv
Default 0
Minimum value 0

noCenter=True | False

when set to True, suppresses centering of the responses and predictors before fitting.

Default False

noCVStdize=True | False

when set to True, suppresses re-centering and rescaling of the responses and predictors when cross validating.

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

noScale=True | False

when set to True, suppresses scaling of the responses and predictors before fitting.

Default False

output={outputOptions}

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

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

* "casOut":{casouttable}

specifies the settings for an output table.

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

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

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

"h":"string"

requests the approximate leverage. If set to an empty string, the prefix H is used for naming the output variable.

"predicted":"string"

requests predicted values for each response. If set to an empty string, the prefix Pred is used for naming the output variables.

Aliases p
pred
"press":"string"

requests approximate predicted residuals for each response. If set to an empty string, the prefix PRESS is used for naming the output variables.

"role":"string"

requests numeric values that indicate the role played by each observation in fitting the model. If set to an empty string, the prefix _ROLE_ is used for naming the output variable.

"t2":"string"

requests scaled sum of squares of score values. If set to an empty string, the prefix TSquare is used for naming the output variable.

Alias tSquare
"xResidual":"string"

requests residuals for each predictor. If set to an empty string, the prefix XResid is used for naming the output variables.

Aliases xr
xResid
"xScore":"string"

requests extracted factors (X-scores, latent vectors, latent variables, T) for each selected model factor. If set to an empty string, the prefix XScore is used for naming the output variables.

"xStd":"string"

requests standardized (centered and scaled) predictor values for each predictor. If set to an empty string, the prefix StdX is used for naming the output variables.

Alias stdX
"xStdsse":"string"

requests the sum of squares of residuals for standardized predictors. If set to an empty string, the prefix StdXSSE is used for naming the output variable.

Aliases xQres
stdXsse
"yResidual":"string"

requests residuals for each response. If set to an empty string, the prefix YResid is used for naming the output variables.

Aliases yr
yResid
"yScore":"string"

requests extracted responses (Y-scores, U) for each selected model factor. If set to an empty string, the prefix YScore is used for naming the output variables.

"yStd":"string"

requests standardized (centered and scaled) response values for each response. If set to an empty string, the prefix StdY is used for naming the output variables.

Alias stdY
"yStdsse":"string"

requests the sum of squares of residuals for standardized responses. If set to an empty string, the prefix StdYSSE is used for naming the output variable.

Aliases yQres
stdYsse

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

partitionByFrac={partByFracStatement}

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

Alias partByFrac

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

partitionByVar={partByVarStatement}

specifies the variable and its values used to partition the data into training and testing roles.

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

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

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

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

Alias poly

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

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

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

* table={castable}

specifies the settings for an input 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.

varss=True | False

when set to True, displays the amount of variation accounted for in each response and predictor.

Default False

pls Action

Fits models by using any one of a number of linear predictive methods, including partial least squares.

R Syntax

results <– cas.pls.pls(s,
attributes=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
cenScale=TRUE | FALSE,
class=list( list(
countMissing=TRUE | FALSE,
descending=TRUE | FALSE,
ignoreMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE,
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
classGlobalOptions=list(
countMissing=TRUE | FALSE,
descending=TRUE | FALSE,
ignoreMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE
),
classLevelsPrint=TRUE | FALSE,
collection=list( list(
details=TRUE | FALSE,
required parameter name="string",
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
cvTest=list(
nSamp=integer,
pValue=double,
seed=integer,
stat="PRESS" | "T2"
),
details=TRUE | FALSE,
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
),
groupbyLimit=64-bit-integer,
inputs=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
required parameter method=list(
algorithm="EIG" | "NIPALS" | "SVD",
epsilon=double,
maxIter=integer,
required parameter name="PCR" | "PLS" | "RRR" | "SIMPLS"
),
model=list(
depVars=list( list(
name="variable-name"
) <, list(...)>),
effects=list( list(
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest=list("string-1" <, "string-2", ...>),
required parameter vars=list("string-1" <, "string-2", ...>)
) <, list(...)>),
intercept=TRUE | FALSE,
solution=TRUE | FALSE
),
multimember=list( list(
details=TRUE | FALSE,
required parameter name="string",
noEffect=TRUE | FALSE,
stdize=TRUE | FALSE,
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>),
weight=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
nClassLevelsPrint=integer,
nFactors=integer,
noCenter=TRUE | FALSE,
noCVStdize=TRUE | FALSE,
nominals=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
noScale=TRUE | FALSE,
output=list(
required parameter casOut=list(
caslib="string"
compress=TRUE | FALSE
indexVars=list("variable-name-1" <, "variable-name-2", ...>)
label="string"
lifetime=64-bit-integer
maxMemSize=64-bit-integer
memoryFormat="DVR" | "INHERIT" | "STANDARD"
name="table-name"
promote=TRUE | FALSE
replace=TRUE | FALSE
replication=integer
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where=list("string-1" <, "string-2", ...>)
),
copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("variable-name-1" <, "variable-name-2", ...>),
h="string",
predicted="string",
press="string",
role="string",
t2="string",
xResidual="string",
xScore="string",
xStd="string",
xStdsse="string",
yResidual="string",
yScore="string",
yStd="string",
yStdsse="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
),
partitionByFrac=list(
seed=integer,
test=double
),
partitionByVar=list(
required parameter name="variable-name",
test="string",
train="string"
),
polynomial=list( list(
degree=integer,
details=TRUE | FALSE,
labelStyle=list(
expand=TRUE | FALSE
exponent="string"
includeName=TRUE | FALSE
productSymbol="NONE" | "string"
),
mDegree=integer,
required parameter name="string",
noSeparate=TRUE | FALSE,
standardize=list(
method="MOMENTS" | "MRANGE" | "WMOMENTS"
options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
prefix="NONE" | "string"
),
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
spline=list( list(
basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",
dataBoundary=TRUE | FALSE,
degree=integer,
details=TRUE | FALSE,
knotMax=double,
knotMethod=list(
equal=integer
list=list(double-1 <, double-2, ...>)
listWithBoundary=list(double-1 <, double-2, ...>)
multiscale=list(
endScale=integer
startScale=integer
)
rangeFractions=list(double-1 <, double-2, ...>)
),
knotMin=double,
required parameter name="string",
naturalCubic=TRUE | FALSE,
separate=TRUE | FALSE,
split=TRUE | FALSE,
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
required parameter table=list(
caslib="string",
computedOnDemand=TRUE | FALSE,
computedVars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter 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",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
groupByMode="NOSORT" | "REDISTRIBUTE",
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters),
required parameter name="table-name",
orderBy=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
singlePass=TRUE | FALSE,
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
where="where-expression",
whereTable=list(
casLib="string"
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)
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)
required parameter name="table-name"
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>)
where="where-expression"
)
),
target="string",
varss=TRUE | FALSE
)
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

specifies the settings for an input table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 output

required parametercasOut

creates a data 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.

Parameter Descriptions

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

cenScale=TRUE | FALSE

when set to True, displays the centering and scaling information.

Default FALSE

class=list( list(classStatement-1) <, list(classStatement-2), ...>)

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

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

Alias classVars

classGlobalOptions=list(classopts)

specifies options that apply to all classification variables.

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

Alias classGlobalOpts

classLevelsPrint=TRUE | FALSE

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

Default TRUE

collection=list( list(collection-1) <, list(collection-2), ...>)

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

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

details=TRUE | FALSE

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

Default FALSE
* name="string"

specifies the name of the effect.

* vars=list("variable-name-1" <, "variable-name-2", ...>)

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.

cvTest=list(cvTestOptions)

performs van der Voet's randomization-based model comparison test.

Long form cvTest=list(stat="PRESS" | "T2")
Shortcut form cvTest="PRESS" | "T2"

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

nSamp=integer

specifies the number of randomizations to perform.

Default 1000
Range 0–MACINT
pValue=double

specifies the cutoff probability for declaring an insignificant difference.

Alias pVal
Default 0.1
Range 0–1
seed=integer

specifies the seed value for the random number stream.

Default 0
Minimum value 0
stat="PRESS" | "T2"

specifies the test statistic for the model comparison. You can specify either T2, for Hotelling's T^2 statistic, or PRESS, for the predicted residual sum of squares.

Default T2

details=TRUE | FALSE

when set to True, displays the details of the fitted model.

Alias detail
Default FALSE

display=list(displayTables)

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

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

groupbyLimit=64-bit-integer

suppresses analysis if the number of BY groups exceeds the specified value.

Minimum value 1

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

* method=list(methodOptions)

specifies the settings for the general factor extraction method.

Long form method=list(name="PCR" | "PLS" | "RRR" | "SIMPLS")
Shortcut form method="PCR" | "PLS" | "RRR" | "SIMPLS"

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

algorithm="EIG" | "NIPALS" | "SVD"

specifies the algorithm used to compute extracted PLS factors.

Alias alg
Default NIPALS
epsilon=double

specifies the convergence criterion for the NIPALS algorithm.

Alias eps
Default 1E-12
Range 0–1
maxIter=integer

specifies the maximum number of iterations for the NIPALS algorithm.

Default 200
Range 0–MACINT
* name="PCR" | "PLS" | "RRR" | "SIMPLS"

specifies the name of the general factor extraction method to use.

model=list(modelOptions)

specifies the responses and the predictors, which determine the Y and X matrices of the model, respectively.

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

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
target
name="variable-name"

names the response variable.

effects=list( list(effect-1) <, list(effect-2), ...>)

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

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

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

specifies the type of interaction for the variables.

Alias interact
Default NONE
maxInteract=integer

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

nest=list("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=list("string-1" <, "string-2", ...>)

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

intercept=TRUE | FALSE

when set to True, includes the intercept term in the model.

Default FALSE
solution=TRUE | FALSE

when set to True, displays the coefficients of the final predictive model for the responses.

Default FALSE

multimember=list( list(multimember-1) <, list(multimember-2), ...>)

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

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

nClassLevelsPrint=integer

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

Minimum value 0

nFactors=integer

specifies the number of factors to extract.

Aliases nFac
lv
Default 0
Minimum value 0

noCenter=TRUE | FALSE

when set to True, suppresses centering of the responses and predictors before fitting.

Default FALSE

noCVStdize=TRUE | FALSE

when set to True, suppresses re-centering and rescaling of the responses and predictors when cross validating.

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

noScale=TRUE | FALSE

when set to True, suppresses scaling of the responses and predictors before fitting.

Default FALSE

output=list(outputOptions)

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

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

* casOut=list(casouttable)

specifies the settings for an output table.

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

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("variable-name-1" <, "variable-name-2", ...>)

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.

h="string"

requests the approximate leverage. If set to an empty string, the prefix H is used for naming the output variable.

predicted="string"

requests predicted values for each response. If set to an empty string, the prefix Pred is used for naming the output variables.

Aliases p
pred
press="string"

requests approximate predicted residuals for each response. If set to an empty string, the prefix PRESS is used for naming the output variables.

role="string"

requests numeric values that indicate the role played by each observation in fitting the model. If set to an empty string, the prefix _ROLE_ is used for naming the output variable.

t2="string"

requests scaled sum of squares of score values. If set to an empty string, the prefix TSquare is used for naming the output variable.

Alias tSquare
xResidual="string"

requests residuals for each predictor. If set to an empty string, the prefix XResid is used for naming the output variables.

Aliases xr
xResid
xScore="string"

requests extracted factors (X-scores, latent vectors, latent variables, T) for each selected model factor. If set to an empty string, the prefix XScore is used for naming the output variables.

xStd="string"

requests standardized (centered and scaled) predictor values for each predictor. If set to an empty string, the prefix StdX is used for naming the output variables.

Alias stdX
xStdsse="string"

requests the sum of squares of residuals for standardized predictors. If set to an empty string, the prefix StdXSSE is used for naming the output variable.

Aliases xQres
stdXsse
yResidual="string"

requests residuals for each response. If set to an empty string, the prefix YResid is used for naming the output variables.

Aliases yr
yResid
yScore="string"

requests extracted responses (Y-scores, U) for each selected model factor. If set to an empty string, the prefix YScore is used for naming the output variables.

yStd="string"

requests standardized (centered and scaled) response values for each response. If set to an empty string, the prefix StdY is used for naming the output variables.

Alias stdY
yStdsse="string"

requests the sum of squares of residuals for standardized responses. If set to an empty string, the prefix StdYSSE is used for naming the output variable.

Aliases yQres
stdYsse

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

partitionByFrac=list(partByFracStatement)

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

Alias partByFrac

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

partitionByVar=list(partByVarStatement)

specifies the variable and its values used to partition the data into training and testing roles.

Alias partByVar
Long form partitionByVar=list(name="variable-name")
Shortcut form partitionByVar="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.

polynomial=list( list(polynomial-1) <, list(polynomial-2), ...>)

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

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

Alias poly

spline=list( list(spline-1) <, list(spline-2), ...>)

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

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

* table=list(castable)

specifies the settings for an input 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.

varss=TRUE | FALSE

when set to True, displays the amount of variation accounted for in each response and predictor.

Default FALSE
Last updated: March 05, 2026