Principal Component Analysis Action Set: Syntax

Provides actions for performing principal component analysis

eig Action

Extracts principal components by using the eigenvalue decomposition method.

CASL Syntax

pca.eig <result=results> <status=rc> /

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

code={

casOut={

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=TRUE | FALSE,

fmtWdth=integer,

iProb=TRUE | FALSE,

indentSize=integer,

intoCutPt=double,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

pCatAll=TRUE | FALSE,

tabForm=TRUE | FALSE

cov=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

freq="variable-name",

groupbyLimit=64-bit-integer,

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

n=integer,

noInt=TRUE | FALSE,

outStat={

casOut={

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

rPrefix="string"

output={

casOut={

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

residual="string",

score="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

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

prefix="string",

singular=double,

std=TRUE | FALSE,

table={

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

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

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

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

name="table-name",

onDemand=TRUE | FALSE,

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

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

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT",

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model.
outStat	required parametercasOut	specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.
output	required parametercasOut	specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.
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, attr

code={aircodegen}

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

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

cov=TRUE | FALSE

when set to True, computes the principal components from the covariance matrix.

Alias	covariance
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).

freq="variable-name"

specifies a numeric variable that contains the frequency of occurrence of each observation.

groupbyLimit=64-bit-integer

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

Minimum value	1

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

specifies the variables to be analyzed. For the NIPALS, ITERGS, and EIG methods, you can analyze only numeric variables. If you omit the parameter, all numeric variables that are not specified in other parameters are analyzed. You must specify either INPUTS or MODEL for the RANDOMPCA method, but you can use both numeric and categorical variables.

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

Alias	input, vars, var

n=integer

specifies the number of principal components to be computed. If the value is 0, all principal components are computed.

Default	0
Minimum value	0

noInt=TRUE | FALSE

when set to True, omits the intercept from the model.

Default	FALSE

outStat={eigOutstat}

specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.

The eigOutstat 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 parameter (Appendix A: Common Parameters).

rPrefix="string"

specifies a prefix for naming the residual variables.

Alias	parPrefix, pPrefix
Default	"R"

output={eigOutput}

specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.

The eigOutput 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 parameter (Appendix A: Common Parameters).

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

copies one or more variables from the input table to the output table.

Alias	copyVar

residual="string"

requests residuals for each analysis variable. The residuals are included only if you also specify the partial parameter. If set to an empty string, the prefix R is used for naming the output variables.

Alias	r, resid

score="string"

specifies principal component scores for each principal component. If the value is an empty string, the prefix Score is used to name the output variables.

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

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

specifies numerical variables to be partialed out if you want to analyze a partial correlation or covariance matrix.

prefix="string"

specifies a prefix for naming the principal components.

Default	"Prin"

singular=double

specifies the singularity criterion.

Alias	sing
Default	1E-08
Range	0–1

std=TRUE | FALSE

when set to True, standardizes to unit variance the principal component scores in the output table that is specified in the output parameter.

Alias	standard
Default	FALSE

* table={castable}

specifies the settings for an input table.

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

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT"

specifies the divisor to use in calculating variances and standard deviations.

Default	DF

weight="variable-name"

specifies a numeric variable that is used as a weight to perform a weighted analysis of the data.

eig Action

Extracts principal components by using the eigenvalue decomposition method.

Lua Syntax

results, info = s:pca_eig{

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

code={

casOut={

caslib="string"

compress=true | false

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=true | false,

fmtWdth=integer,

iProb=true | false,

indentSize=integer,

intoCutPt=double,

labelId=integer,

lineSize=integer,

noTrim=true | false,

pCatAll=true | false,

tabForm=true | false

cov=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

freq="variable-name",

groupbyLimit=64-bit-integer,

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

n=integer,

noInt=true | false,

outStat={

casOut={

caslib="string"

compress=true | false

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

rPrefix="string"

output={

casOut={

caslib="string"

compress=true | false

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

residual="string",

score="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

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

prefix="string",

singular=double,

std=true | false,

table={

caslib="string",

computedOnDemand=true | false,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

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

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

onDemand=true | false,

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=true | false,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT",

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model.
outStat	required parametercasOut	specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.
output	required parametercasOut	specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.
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, attr

code={aircodegen}

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

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

cov=true | false

when set to True, computes the principal components from the covariance matrix.

Alias	covariance
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).

freq="variable-name"

specifies a numeric variable that contains the frequency of occurrence of each observation.

groupbyLimit=64-bit-integer

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

Minimum value	1

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

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

Alias	input, vars, var

n=integer

specifies the number of principal components to be computed. If the value is 0, all principal components are computed.

Default	0
Minimum value	0

noInt=true | false

when set to True, omits the intercept from the model.

Default	false

outStat={eigOutstat}

specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.

The eigOutstat 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 parameter (Appendix A: Common Parameters).

rPrefix="string"

specifies a prefix for naming the residual variables.

Alias	parPrefix, pPrefix
Default	"R"

output={eigOutput}

specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.

The eigOutput 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 parameter (Appendix A: Common Parameters).

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

copies one or more variables from the input table to the output table.

Alias	copyVar

residual="string"

Alias	r, resid

score="string"

specifies principal component scores for each principal component. If the value is an empty string, the prefix Score is used to name the output variables.

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

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

specifies numerical variables to be partialed out if you want to analyze a partial correlation or covariance matrix.

prefix="string"

specifies a prefix for naming the principal components.

Default	"Prin"

singular=double

specifies the singularity criterion.

Alias	sing
Default	1E-08
Range	0–1

std=true | false

when set to True, standardizes to unit variance the principal component scores in the output table that is specified in the output parameter.

Alias	standard
Default	false

* table={castable}

specifies the settings for an input table.

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

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT"

specifies the divisor to use in calculating variances and standard deviations.

Default	DF

weight="variable-name"

specifies a numeric variable that is used as a weight to perform a weighted analysis of the data.

eig Action

Extracts principal components by using the eigenvalue decomposition method.

Python Syntax

results= s.pca.eig(

attributes=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

code={

"casOut":{

"caslib":"string"

"compress":True | False

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

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

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

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

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

"comment":True | False,

"fmtWdth":integer,

"iProb":True | False,

"indentSize":integer,

"intoCutPt":double,

"labelId":integer,

"lineSize":integer,

"noTrim":True | False,

"pCatAll":True | False,

"tabForm":True | False

cov=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

freq="variable-name",

groupbyLimit=64-bit-integer,

inputs=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

n=integer,

noInt=True | False,

outStat={

"casOut":{

"caslib":"string"

"compress":True | False

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

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

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

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

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

"rPrefix":"string"

output={

"casOut":{

"caslib":"string"

"compress":True | False

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

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

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

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

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

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

"residual":"string",

"score":"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

partial=["variable-name-1" <, "variable-name-2", ...>],

prefix="string",

singular=double,

std=True | False,

table={

"caslib":"string",

"computedOnDemand":True | False,

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"computedVarsProgram":"string",

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

"groupBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"groupByMode":"NOSORT" | "REDISTRIBUTE",

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

"name":"table-name",

"onDemand":True | False,

"orderBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT",

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model.
outStat	required parametercasOut	specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.
output	required parametercasOut	specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.
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, attr

code={aircodegen}

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

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

cov=True | False

when set to True, computes the principal components from the covariance matrix.

Alias	covariance
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).

freq="variable-name"

specifies a numeric variable that contains the frequency of occurrence of each observation.

groupbyLimit=64-bit-integer

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

Minimum value	1

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

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

Alias	input, vars, var

n=integer

specifies the number of principal components to be computed. If the value is 0, all principal components are computed.

Default	0
Minimum value	0

noInt=True | False

when set to True, omits the intercept from the model.

Default	False

outStat={eigOutstat}

specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.

The eigOutstat 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 parameter (Appendix A: Common Parameters).

"rPrefix":"string"

specifies a prefix for naming the residual variables.

Alias	parPrefix, pPrefix
Default	"R"

output={eigOutput}

specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.

The eigOutput 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 parameter (Appendix A: Common Parameters).

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

copies one or more variables from the input table to the output table.

Alias	copyVar

"residual":"string"

Alias	r, resid

"score":"string"

specifies principal component scores for each principal component. If the value is an empty string, the prefix Score is used to name the output variables.

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

partial=["variable-name-1" <, "variable-name-2", ...>]

specifies numerical variables to be partialed out if you want to analyze a partial correlation or covariance matrix.

prefix="string"

specifies a prefix for naming the principal components.

Default	"Prin"

singular=double

specifies the singularity criterion.

Alias	sing
Default	1E-08
Range	0–1

std=True | False

when set to True, standardizes to unit variance the principal component scores in the output table that is specified in the output parameter.

Alias	standard
Default	False

* table={castable}

specifies the settings for an input table.

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

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT"

specifies the divisor to use in calculating variances and standard deviations.

Default	DF

weight="variable-name"

specifies a numeric variable that is used as a weight to perform a weighted analysis of the data.

eig Action

Extracts principal components by using the eigenvalue decomposition method.

R Syntax

results <– cas.pca.eig(s,

attributes=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

code=list(

casOut=list(

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=TRUE | FALSE,

fmtWdth=integer,

iProb=TRUE | FALSE,

indentSize=integer,

intoCutPt=double,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

pCatAll=TRUE | FALSE,

tabForm=TRUE | FALSE

cov=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

freq="variable-name",

groupbyLimit=64-bit-integer,

inputs=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

n=integer,

noInt=TRUE | FALSE,

outStat=list(

casOut=list(

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

rPrefix="string"

output=list(

casOut=list(

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

residual="string",

score="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

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

prefix="string",

singular=double,

std=TRUE | FALSE,

table=list(

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

computedVarsProgram="string",

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),

groupBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

onDemand=TRUE | FALSE,

orderBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

singlePass=TRUE | FALSE,

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

where="where-expression",

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT",

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model.
outStat	required parametercasOut	specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.
output	required parametercasOut	specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.
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, attr

code=list(aircodegen)

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

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

cov=TRUE | FALSE

when set to True, computes the principal components from the covariance matrix.

Alias	covariance
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).

freq="variable-name"

specifies a numeric variable that contains the frequency of occurrence of each observation.

groupbyLimit=64-bit-integer

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

Minimum value	1

inputs=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

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

Alias	input, vars, var

n=integer

specifies the number of principal components to be computed. If the value is 0, all principal components are computed.

Default	0
Minimum value	0

noInt=TRUE | FALSE

when set to True, omits the intercept from the model.

Default	FALSE

outStat=list(eigOutstat)

specifies the output table to be created to contain various statistics, including means, standard deviations, eigenvalues, and eigenvectors.

The eigOutstat 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 parameter (Appendix A: Common Parameters).

rPrefix="string"

specifies a prefix for naming the residual variables.

Alias	parPrefix, pPrefix
Default	"R"

output=list(eigOutput)

specifies the output table to be created to contain observationwise statistics. If you do not specify any statistic, then only the principal component scores are included.

The eigOutput 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 parameter (Appendix A: Common Parameters).

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

copies one or more variables from the input table to the output table.

Alias	copyVar

residual="string"

Alias	r, resid

score="string"

specifies principal component scores for each principal component. If the value is an empty string, the prefix Score is used to name the output variables.

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

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

specifies numerical variables to be partialed out if you want to analyze a partial correlation or covariance matrix.

prefix="string"

specifies a prefix for naming the principal components.

Default	"Prin"

singular=double

specifies the singularity criterion.

Alias	sing
Default	1E-08
Range	0–1

std=TRUE | FALSE

when set to True, standardizes to unit variance the principal component scores in the output table that is specified in the output parameter.

Alias	standard
Default	FALSE

* table=list(castable)

specifies the settings for an input table.

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

varDef="DF" | "N" | "WDF" | "WEIGHT" | "WGT"

specifies the divisor to use in calculating variances and standard deviations.

Default	DF

weight="variable-name"

specifies a numeric variable that is used as a weight to perform a weighted analysis of the data.

Last updated: November 02, 2020