Bayesian Additive Regression Trees Action Set

Provides actions for fitting Bayesian additive regression trees models

bartScoreMargin Action

Computes predictive margins by using a fitted Bayesian additive regression trees model..

CASL Syntax

bart.bartScoreMargin <result=results> <status=rc> /

alpha=double,

casOut={

caslib="string",

compress=TRUE | FALSE,

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

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

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

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

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

differences={{

evtMargin="string",

label="string",

name="string",

refMargin="string"

}, {...}},

marginInfo=TRUE | FALSE,

margins={{

at={{

value="string" | double,

var="string"

}, {...}},

label="string",

name="string"

}, {...}},

model={

caslib="string",

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

name="table-name",

whereTable={

casLib="string"

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 | 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}

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

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

seed=64-bit-integer,

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"

}

;

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 parametermodel	—	specifies a binary table object from a previous model fitting.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the settings for an output data table.

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all equal-tail credible limits.

Default	0.05
Range	(0, 1)

casOut={casouttable}

specifies the settings for an output data table.

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

differences={{bartScoreMargin_scoreDiff-1} <, {bartScoreMargin_scoreDiff-2}, ...>}

specifies differences of predictive margins.

Alias	diffs

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

* evtMargin="string"

specifies the event predictive margin by its name.

Alias	evtScen

label="string"

labels the difference in predictive margins in output tables.

name="string"

names the difference in predictive margins in output tables.

* refMargin="string"

specifies the reference predictive margin by its name.

Alias	refScen

marginInfo=TRUE | FALSE

requests a table summarizing the variables that define the predictive margin and the values they are set to.

Default	FALSE

* margins={{bartScoreMargin_evaluate-1} <, {bartScoreMargin_evaluate-2}, ...>}

specifies a predictive margin.

Alias	scenarios

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

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

specifies the variables to modify in a predictive margin and the values they are set to.

Alias	evaluate

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

* value="string" | double

specifies the value a variable is set to in the predictive margin. For continuous variables, a numeric value is specified. For classification variables, the formatted level is specified.

* var="string"

names a variable to modify in a predictive margin.

Alias	variable

label="string"

labels the predictive margin in output tables.

* name="string"

names the predictive margin in output tables.

* model={castable}

specifies a binary table object from a previous model fitting.

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

Aliases	fit
	load
	restore

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

caslib="string"

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

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

specifies data source options.

Aliases	options
Aliases	dataSource

* name="table-name"

specifies the name of the input table.

whereTable={groupbytable}

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

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

casLib="string"

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

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | 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}

specifies data source options.

Aliases	options
Aliases	dataSource

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

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

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

Alias	import

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

* name="table-name"

specifies the name of the filter table.

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

specifies the variable names to use from the filter table.

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

format="string"

specifies the format to apply to the variable.

formattedLength=integer

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

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

* table={castable}

specifies the input data table.

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

bartScoreMargin Action

Computes predictive margins by using a fitted Bayesian additive regression trees model..

Lua Syntax

results, info = s:bart_bartScoreMargin{

alpha=double,

casOut={

caslib="string",

compress=true | false,

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

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

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

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

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

differences={{

evtMargin="string",

label="string",

name="string",

refMargin="string"

}, {...}},

marginInfo=true | false,

margins={{

at={{

value="string" | double,

var="string"

}, {...}},

label="string",

name="string"

}, {...}},

model={

caslib="string",

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

name="table-name",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

seed=64-bit-integer,

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"

}

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 parametermodel	—	specifies a binary table object from a previous model fitting.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the settings for an output data table.

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all equal-tail credible limits.

Default	0.05
Range	(0, 1)

casOut={casouttable}

specifies the settings for an output data table.

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

differences={{bartScoreMargin_scoreDiff-1} <, {bartScoreMargin_scoreDiff-2}, ...>}

specifies differences of predictive margins.

Alias	diffs

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

* evtMargin="string"

specifies the event predictive margin by its name.

Alias	evtScen

label="string"

labels the difference in predictive margins in output tables.

name="string"

names the difference in predictive margins in output tables.

* refMargin="string"

specifies the reference predictive margin by its name.

Alias	refScen

marginInfo=true | false

requests a table summarizing the variables that define the predictive margin and the values they are set to.

Default	false

* margins={{bartScoreMargin_evaluate-1} <, {bartScoreMargin_evaluate-2}, ...>}

specifies a predictive margin.

Alias	scenarios

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

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

specifies the variables to modify in a predictive margin and the values they are set to.

Alias	evaluate

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

* value="string" | double

specifies the value a variable is set to in the predictive margin. For continuous variables, a numeric value is specified. For classification variables, the formatted level is specified.

* var="string"

names a variable to modify in a predictive margin.

Alias	variable

label="string"

labels the predictive margin in output tables.

* name="string"

names the predictive margin in output tables.

* model={castable}

specifies a binary table object from a previous model fitting.

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

Aliases	fit
	load
	restore

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

caslib="string"

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

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

specifies data source options.

Aliases	options
Aliases	dataSource

* name="table-name"

specifies the name of the input table.

whereTable={groupbytable}

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

casLib="string"

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

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | 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}

specifies data source options.

Aliases	options
Aliases	dataSource

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

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

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

Alias	import

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

* name="table-name"

specifies the name of the filter table.

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

specifies the variable names to use from the filter table.

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

format="string"

specifies the format to apply to the variable.

formattedLength=integer

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

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

* table={castable}

specifies the input data table.

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

bartScoreMargin Action

Computes predictive margins by using a fitted Bayesian additive regression trees model..

Python Syntax

results= s.bart.bartScoreMargin(

alpha=double,

casOut={

"caslib":"string",

"compress":True | False,

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

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

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

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer,

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

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

differences=[{

"evtMargin":"string",

"label":"string",

"name":"string",

"refMargin":"string"

}<, {...}>],

marginInfo=True | False,

margins=[{

"at":[{

"value":"string" | double,

"var":"string"

}<, {...}>],

"label":"string",

"name":"string"

}<, {...}>],

model={

"caslib":"string",

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

"name":"table-name",

"whereTable":{

"casLib":"string"

"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 | 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}

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

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

seed=64-bit-integer,

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"

}

)

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 parametermodel	—	specifies a binary table object from a previous model fitting.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the settings for an output data table.

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all equal-tail credible limits.

Default	0.05
Range	(0, 1)

casOut={casouttable}

specifies the settings for an output data table.

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

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

specifies differences of predictive margins.

Alias	diffs

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

* "evtMargin":"string"

specifies the event predictive margin by its name.

Alias	evtScen

"label":"string"

labels the difference in predictive margins in output tables.

"name":"string"

names the difference in predictive margins in output tables.

* "refMargin":"string"

specifies the reference predictive margin by its name.

Alias	refScen

marginInfo=True | False

requests a table summarizing the variables that define the predictive margin and the values they are set to.

Default	False

* margins=[{bartScoreMargin_evaluate-1} <, {bartScoreMargin_evaluate-2}, ...>]

specifies a predictive margin.

Alias	scenarios

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

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

specifies the variables to modify in a predictive margin and the values they are set to.

Alias	evaluate

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

* "value":"string" | double

specifies the value a variable is set to in the predictive margin. For continuous variables, a numeric value is specified. For classification variables, the formatted level is specified.

* "var":"string"

names a variable to modify in a predictive margin.

Alias	variable

"label":"string"

labels the predictive margin in output tables.

* "name":"string"

names the predictive margin in output tables.

* model={castable}

specifies a binary table object from a previous model fitting.

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

Aliases	fit
	load
	restore

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

"caslib":"string"

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

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

specifies data source options.

Aliases	options
Aliases	dataSource

* "name":"table-name"

specifies the name of the input table.

"whereTable":{groupbytable}

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

"casLib":"string"

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

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | 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}

specifies data source options.

Aliases	options
Aliases	dataSource

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

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

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

Alias	import_

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

* "name":"table-name"

specifies the name of the filter table.

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

specifies the variable names to use from the filter table.

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

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

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

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

* table={castable}

specifies the input data table.

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

bartScoreMargin Action

Computes predictive margins by using a fitted Bayesian additive regression trees model..

R Syntax

results <– cas.bart.bartScoreMargin(s,

alpha=double,

casOut=list(

caslib="string",

compress=TRUE | FALSE,

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

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

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

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

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

differences=list( list(

evtMargin="string",

label="string",

name="string",

refMargin="string"

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

marginInfo=TRUE | FALSE,

margins=list( list(

at=list( list(

value="string" | double,

var="string"

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

label="string",

name="string"

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

model=list(

caslib="string",

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

name="table-name",

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

seed=64-bit-integer,

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"

)

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 parametermodel	—	specifies a binary table object from a previous model fitting.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the settings for an output data table.

Parameter Descriptions

alpha=double

specifies the significance level to use for the construction of all equal-tail credible limits.

Default	0.05
Range	(0, 1)

casOut=list(casouttable)

specifies the settings for an output data table.

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

differences=list( list(bartScoreMargin_scoreDiff-1) <, list(bartScoreMargin_scoreDiff-2), ...>)

specifies differences of predictive margins.

Alias	diffs

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

* evtMargin="string"

specifies the event predictive margin by its name.

Alias	evtScen

label="string"

labels the difference in predictive margins in output tables.

name="string"

names the difference in predictive margins in output tables.

* refMargin="string"

specifies the reference predictive margin by its name.

Alias	refScen

marginInfo=TRUE | FALSE

requests a table summarizing the variables that define the predictive margin and the values they are set to.

Default	FALSE

* margins=list( list(bartScoreMargin_evaluate-1) <, list(bartScoreMargin_evaluate-2), ...>)

specifies a predictive margin.

Alias	scenarios

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

at=list( list(bartScoreMargin_varValue-1) <, list(bartScoreMargin_varValue-2), ...>)

specifies the variables to modify in a predictive margin and the values they are set to.

Alias	evaluate

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

* value="string" | double

specifies the value a variable is set to in the predictive margin. For continuous variables, a numeric value is specified. For classification variables, the formatted level is specified.

* var="string"

names a variable to modify in a predictive margin.

Alias	variable

label="string"

labels the predictive margin in output tables.

* name="string"

names the predictive margin in output tables.

* model=list(castable)

specifies a binary table object from a previous model fitting.

Long form	model=list(name="table-name")
Shortcut form	model="table-name"

Aliases	fit
	load
	restore

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

caslib="string"

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

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

specifies data source options.

Aliases	options
Aliases	dataSource

* name="table-name"

specifies the name of the input table.

whereTable=list(groupbytable)

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

casLib="string"

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

dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | 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)

specifies data source options.

Aliases	options
Aliases	dataSource

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

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

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

Alias	import

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

* name="table-name"

specifies the name of the filter table.

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

specifies the variable names to use from the filter table.

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

format="string"

specifies the format to apply to the variable.

formattedLength=integer

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

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

* table=list(castable)

specifies the input data table.

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

Last updated: September 13, 2022