Dimension Reduction Action Set

Provides actions for performing supervised and unsupervised dimension reduction

super Action

Provides an action for performing supervised dimension reduction.

CASL Syntax

varReduce.super <result=results> <status=rc> /
AIC=TRUE | FALSE,
AICC=TRUE | FALSE,
analysis="DSC" | "VAR",
attributes={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
BIC=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", ...>}
}, {...}},
classglobalopts={
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
},
collection={{
details=TRUE | FALSE,
required parameter name="string",
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
diagnostics={
eyecatcher="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
},
freq="variable-name",
inputs={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
maxeffects=64-bit-integer,
maxsteps=64-bit-integer,
model={
censor="variable-name",
depVars={{
name="variable-name",
options={modelopts}
}, {...}},
dist="BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL",
effects={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
entry="variable-name",
group={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
include=integer | {{effect-1} <, {effect-2}, ...>},
informative=TRUE | FALSE,
link="CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO",
noint=TRUE | FALSE,
offset="variable-name",
start=integer | {{effect-1} <, {effect-2}, ...>},
subject={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
trial="variable-name"
},
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", ...>}
}, {...}},
nominals={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
outcp={
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", ...>}
},
eps=double,
list=TRUE | FALSE
},
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
},
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", ...>}
}, {...}},
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",
tech="CORR" | "COV" | "SSCP",
varexp=double,
varinc=double,
weight="variable-name"
;
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

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

Parameter

Subparameter

Description

 outcp

required parametercasOut

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

 outputTables

names

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

Parameter Descriptions

AIC=TRUE | FALSE

performs model selection by using Akaike's information criterion.

Default FALSE

AICC=TRUE | FALSE

performs model selection by using the corrected Akaike's information criterion.

Default FALSE

analysis="DSC" | "VAR"

specifies the variable selection technique. VAR performs variance analysis for variable selection; DSC performs discriminant analysis for variable selection.

Default VAR

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

BIC=TRUE | FALSE

performs model selection by using the Schwarz Bayesian information criterion.

Default FALSE

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

names 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

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).

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.

diagnostics={_diagnostics}

eyecatcher="string"

specifies a quoted string that will be prefixed to any messages that are associated with this action invocation.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for 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

maxeffects=64-bit-integer

specifies the number of effects to select; the number must be greater than or equal to 1.

Default 200
Range 0–100000

maxsteps=64-bit-integer

specifies the maximum number of steps to take; the number must be greater than or equal to 1.

Default 0
Range 0–100000

model={modelStatement}

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

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

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).

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

outcp={OutputCPStatement}

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

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

* casOut={casouttable}

specifies the output table.

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

eps=double

specifies an epsilon value such that matrix entries that have an absolute value smaller than epsilon are ignored in the output. You must specify the list parameter when you specify the eps parameter.

Default 0
Minimum value 0
list=TRUE | FALSE

outputs the symmetric matrix in the list-of-lists (LIL) format.

Default FALSE

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

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

* 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.

tech="CORR" | "COV" | "SSCP"

specifies the matrix to use to select variables: Pearson correlation (CORR), sum of squares and crossproducts (SSCP), or covariance (COV), respectively.

Default CORR

varexp=double

specifies the fraction of the total variance to be explained; the value must be between 0 and 1.

Default 0.9

varinc=double

specifies the minimum increment of explained variance (in a fraction of the total variance); the value must be between 0 and 1.

Default 0

weight="variable-name"

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

super Action

Provides an action for performing supervised dimension reduction.

Lua Syntax

results, info = s:varReduce_super{
AIC=true | false,
AICC=true | false,
analysis="DSC" | "VAR",
attributes={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
BIC=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", ...>}
}, {...}},
classglobalopts={
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
},
collection={{
details=true | false,
required parameter name="string",
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
diagnostics={
eyecatcher="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
},
freq="variable-name",
inputs={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
maxeffects=64-bit-integer,
maxsteps=64-bit-integer,
model={
censor="variable-name",
depVars={{
name="variable-name",
options={modelopts}
}, {...}},
dist="BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL",
effects={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
entry="variable-name",
group={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
include=integer | {{effect-1} <, {effect-2}, ...>},
informative=true | false,
link="CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO",
noint=true | false,
offset="variable-name",
start=integer | {{effect-1} <, {effect-2}, ...>},
subject={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter vars={"string-1" <, "string-2", ...>}
}, {...}},
trial="variable-name"
},
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", ...>}
}, {...}},
nominals={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
outcp={
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", ...>}
},
eps=double,
list=true | false
},
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
},
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", ...>}
}, {...}},
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",
tech="CORR" | "COV" | "SSCP",
varexp=double,
varinc=double,
weight="variable-name"
}
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

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

Parameter

Subparameter

Description

 outcp

required parametercasOut

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

 outputTables

names

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

Parameter Descriptions

AIC=true | false

performs model selection by using Akaike's information criterion.

Default false

AICC=true | false

performs model selection by using the corrected Akaike's information criterion.

Default false

analysis="DSC" | "VAR"

specifies the variable selection technique. VAR performs variance analysis for variable selection; DSC performs discriminant analysis for variable selection.

Default VAR

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

BIC=true | false

performs model selection by using the Schwarz Bayesian information criterion.

Default false

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

names 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

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).

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.

diagnostics={_diagnostics}

eyecatcher="string"

specifies a quoted string that will be prefixed to any messages that are associated with this action invocation.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for 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

maxeffects=64-bit-integer

specifies the number of effects to select; the number must be greater than or equal to 1.

Default 200
Range 0–100000

maxsteps=64-bit-integer

specifies the maximum number of steps to take; the number must be greater than or equal to 1.

Default 0
Range 0–100000

model={modelStatement}

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

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

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).

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

outcp={OutputCPStatement}

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

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

* casOut={casouttable}

specifies the output table.

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

eps=double

specifies an epsilon value such that matrix entries that have an absolute value smaller than epsilon are ignored in the output. You must specify the list parameter when you specify the eps parameter.

Default 0
Minimum value 0
list=true | false

outputs the symmetric matrix in the list-of-lists (LIL) format.

Default false

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

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

* 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.

tech="CORR" | "COV" | "SSCP"

specifies the matrix to use to select variables: Pearson correlation (CORR), sum of squares and crossproducts (SSCP), or covariance (COV), respectively.

Default CORR

varexp=double

specifies the fraction of the total variance to be explained; the value must be between 0 and 1.

Default 0.9

varinc=double

specifies the minimum increment of explained variance (in a fraction of the total variance); the value must be between 0 and 1.

Default 0

weight="variable-name"

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

super Action

Provides an action for performing supervised dimension reduction.

Python Syntax

results=s.varReduce.super(
AIC=True | False,
AICC=True | False,
analysis="DSC" | "VAR",
attributes=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
BIC=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", ...>]
}<, {...}>],
classglobalopts={
"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
},
collection=[{
"details":True | False,
required parameter "name":"string",
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
diagnostics={
"eyecatcher":"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
},
freq="variable-name",
inputs=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
maxeffects=64-bit-integer,
maxsteps=64-bit-integer,
model={
"censor":"variable-name",
"depVars":[{
"name":"variable-name",
"options":{modelopts}
}<, {...}>],
"dist":"BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL",
"effects":[{
"interaction":"BAR" | "CROSS" | "NONE",
"maxInteract":integer,
"nest":["string-1" <, "string-2", ...>],
required parameter "vars":["string-1" <, "string-2", ...>]
}<, {...}>],
"entry":"variable-name",
"group":[{
"interaction":"BAR" | "CROSS" | "NONE",
"maxInteract":integer,
"nest":["string-1" <, "string-2", ...>],
required parameter "vars":["string-1" <, "string-2", ...>]
}<, {...}>],
"include":integer | [{effect-1} <, {effect-2}, ...>],
"informative":True | False,
"link":"CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO",
"noint":True | False,
"offset":"variable-name",
"start":integer | [{effect-1} <, {effect-2}, ...>],
"subject":[{
"interaction":"BAR" | "CROSS" | "NONE",
"maxInteract":integer,
"nest":["string-1" <, "string-2", ...>],
required parameter "vars":["string-1" <, "string-2", ...>]
}<, {...}>],
"trial":"variable-name"
},
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", ...>]
}<, {...}>],
nominals=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
outcp={
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", ...>]
},
"eps":double,
"list":True | False
},
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
},
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", ...>]
}<, {...}>],
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",
tech="CORR" | "COV" | "SSCP",
varexp=double,
varinc=double,
weight="variable-name"
)
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

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

Parameter

Subparameter

Description

 outcp

required parametercasOut

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

 outputTables

names

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

Parameter Descriptions

AIC=True | False

performs model selection by using Akaike's information criterion.

Default False

AICC=True | False

performs model selection by using the corrected Akaike's information criterion.

Default False

analysis="DSC" | "VAR"

specifies the variable selection technique. VAR performs variance analysis for variable selection; DSC performs discriminant analysis for variable selection.

Default VAR

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

BIC=True | False

performs model selection by using the Schwarz Bayesian information criterion.

Default False

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

names 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

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).

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.

diagnostics={_diagnostics}

"eyecatcher":"string"

specifies a quoted string that will be prefixed to any messages that are associated with this action invocation.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for 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

maxeffects=64-bit-integer

specifies the number of effects to select; the number must be greater than or equal to 1.

Default 200
Range 0–100000

maxsteps=64-bit-integer

specifies the maximum number of steps to take; the number must be greater than or equal to 1.

Default 0
Range 0–100000

model={modelStatement}

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

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

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).

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

outcp={OutputCPStatement}

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

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

* "casOut":{casouttable}

specifies the output table.

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

"eps":double

specifies an epsilon value such that matrix entries that have an absolute value smaller than epsilon are ignored in the output. You must specify the list parameter when you specify the eps parameter.

Default 0
Minimum value 0
"list":True | False

outputs the symmetric matrix in the list-of-lists (LIL) format.

Default False

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

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

* 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.

tech="CORR" | "COV" | "SSCP"

specifies the matrix to use to select variables: Pearson correlation (CORR), sum of squares and crossproducts (SSCP), or covariance (COV), respectively.

Default CORR

varexp=double

specifies the fraction of the total variance to be explained; the value must be between 0 and 1.

Default 0.9

varinc=double

specifies the minimum increment of explained variance (in a fraction of the total variance); the value must be between 0 and 1.

Default 0

weight="variable-name"

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

super Action

Provides an action for performing supervised dimension reduction.

R Syntax

results <– cas.varReduce.super(s,
AIC=TRUE | FALSE,
AICC=TRUE | FALSE,
analysis="DSC" | "VAR",
attributes=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
BIC=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(...)>),
classglobalopts=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
),
collection=list( list(
details=TRUE | FALSE,
required parameter name="string",
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
diagnostics=list(
eyecatcher="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
),
freq="variable-name",
inputs=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
maxeffects=64-bit-integer,
maxsteps=64-bit-integer,
model=list(
censor="variable-name",
depVars=list( list(
name="variable-name",
options=list(modelopts)
) <, list(...)>),
dist="BERNOULLI" | "BETA" | "BINOMIAL" | "EXPONENTIAL" | "GAMMA" | "GAUSSIAN" | "GENPOISSON" | "GEOMETRIC" | "IGAUSSIAN" | "MULTINOMIAL" | "NEGBINOMIAL" | "POISSON" | "STUDENT" | "TWEEDIE" | "WEIBULL",
effects=list( list(
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest=list("string-1" <, "string-2", ...>),
required parameter vars=list("string-1" <, "string-2", ...>)
) <, list(...)>),
entry="variable-name",
group=list( list(
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest=list("string-1" <, "string-2", ...>),
required parameter vars=list("string-1" <, "string-2", ...>)
) <, list(...)>),
include=integer | list( list(effect-1) <, list(effect-2), ...>),
informative=TRUE | FALSE,
link="CLOGLOG" | "GLOGIT" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWER" | "POWERMINUS2" | "RECIPROCAL" | "ZERO",
noint=TRUE | FALSE,
offset="variable-name",
start=integer | list( list(effect-1) <, list(effect-2), ...>),
subject=list( list(
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest=list("string-1" <, "string-2", ...>),
required parameter vars=list("string-1" <, "string-2", ...>)
) <, list(...)>),
trial="variable-name"
),
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(...)>),
nominals=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
outcp=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", ...>)
),
eps=double,
list=TRUE | FALSE
),
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
),
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(...)>),
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",
tech="CORR" | "COV" | "SSCP",
varexp=double,
varinc=double,
weight="variable-name"
)
indicates a required parameter

Summary: Input and Output Tables

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

Parameters for Reading Input Tables

Parameter

Subparameter

Description

required parametertable

—

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

Parameter

Subparameter

Description

 outcp

required parametercasOut

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

 outputTables

names

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

Parameter Descriptions

AIC=TRUE | FALSE

performs model selection by using Akaike's information criterion.

Default FALSE

AICC=TRUE | FALSE

performs model selection by using the corrected Akaike's information criterion.

Default FALSE

analysis="DSC" | "VAR"

specifies the variable selection technique. VAR performs variance analysis for variable selection; DSC performs discriminant analysis for variable selection.

Default VAR

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

BIC=TRUE | FALSE

performs model selection by using the Schwarz Bayesian information criterion.

Default FALSE

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 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).

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.

diagnostics=list(_diagnostics)

eyecatcher="string"

specifies a quoted string that will be prefixed to any messages that are associated with this action invocation.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for 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

maxeffects=64-bit-integer

specifies the number of effects to select; the number must be greater than or equal to 1.

Default 200
Range 0–100000

maxsteps=64-bit-integer

specifies the maximum number of steps to take; the number must be greater than or equal to 1.

Default 0
Range 0–100000

model=list(modelStatement)

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

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

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).

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

outcp=list(OutputCPStatement)

creates a data set that contains a symmetric matrix that depicts the relationships among variables and also a set of statistics about the input data set and variables.

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

* casOut=list(casouttable)

specifies the output table.

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

eps=double

specifies an epsilon value such that matrix entries that have an absolute value smaller than epsilon are ignored in the output. You must specify the list parameter when you specify the eps parameter.

Default 0
Minimum value 0
list=TRUE | FALSE

outputs the symmetric matrix in the list-of-lists (LIL) format.

Default FALSE

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

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

* 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.

tech="CORR" | "COV" | "SSCP"

specifies the matrix to use to select variables: Pearson correlation (CORR), sum of squares and crossproducts (SSCP), or covariance (COV), respectively.

Default CORR

varexp=double

specifies the fraction of the total variance to be explained; the value must be between 0 and 1.

Default 0.9

varinc=double

specifies the minimum increment of explained variance (in a fraction of the total variance); the value must be between 0 and 1.

Default 0

weight="variable-name"

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

Last updated: March 05, 2026