Generalized Additive Models Action Set

gam.gampl <result=results> <status=rc> /

alpha=double,

={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

={{

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string",

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

}, {...}},

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string"

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",

itDetails=TRUE | FALSE,

maxNameLen=integer | "MAX" | "MIN" | "SASV9",

allObs=TRUE | FALSE,

criterion="DEFAULT" | "GACV" | "GCV" | "UBRE",

critFactor=double,

depVars

={{

name="variable-name",

options={modelopts}

}, {...}},

effects

={{

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

maxInteract=integer,

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

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

}, {...}},

fdHessian=TRUE | FALSE,

initialPhi=double,

maxPhi=double,

method="OUTER" | "PERFORMANCE",

minPhi=double,

normalize=TRUE | FALSE,

offset="variable-name",

phi=double,

ridge=double,

scale="DEVIANCE" | "MLE" | "PEARSON",

splines

={{

details_knots=TRUE | FALSE,

details_penalty=TRUE | FALSE,

details_summary=TRUE | FALSE,

df=integer,

initSmooth=double,

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

m=integer,

maxDf=integer,

maxKnots=integer,

maxSmooth=double,

minSmooth=double,

smooth=double,

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

}, {...}},

trial="variable-name",

tweedieInitialP=double,

tweedieOffset=double,

tweedieP=double

nClassLevelsPrint=integer,

noPrint=TRUE | FALSE,

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", ...>}

component=TRUE | FALSE,

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

into="string",

intoCutpt=double,

lower="string",

pearson="string",

pred="string",

resid="string",

std="string",

upper="string",

xBeta="string"

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

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

absConv=double,

absFConv=double,

absFConv_n=integer,

absGConv=double,

absGConv_n=integer,

fConv=double,

fConv_n=integer,

gConv=double,

gConv_n=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

seed=64-bit-integer,

singChol=double,

singular=double,

absConv=double,

absFConv=double,

absFConv_n=integer,

absGConv=double,

absGConv_n=integer,

fConv=double,

fConv_n=integer,

gConv=double,

gConv_n=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

caslib="string",

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

label="string",

lifetime=64-bit-integer,

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

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer

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 | 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 | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

name="table-name"

vars

={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

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 input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after the model is fit.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	creates a binary table object on the server that contains model fit information so that you can use it later for scoring.

Parameter Descriptions

alpha=double

specifies the significance level to use for hypothesis testing of regression parameters and spline effects, and for the construction of all confidence bands.

Default	0.05
Range	(0, 1)

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

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

names the classification variables to use as explanatory variables in the analysis.

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

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level for this variable.

Default	FALSE

descending=TRUE | FALSE

when set to True, reverses the sort order that is imposed by the order parameter.

Default	FALSE

ignoreMissing=TRUE | FALSE

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	FALSE

levelizeRaw=TRUE | FALSE

when set to True, bases levelization for this variable on raw values.

Default	FALSE

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

specifies the parameterization method for the classification variable or variables. The default is GLM when none of the variables specified in the vars parameter includes a param parameter; otherwise, the default is REFERENCE.

ref="FIRST" | "LAST" | double | "string"

specifies the reference level to use when you specify a nonsingular parameterization in the param parameter. For an individual variable, you can specify the level of the variable to use as the reference level. If the action supports the global class options parameter, then you can specify FIRST or LAST.

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

specifies the classification variables.

Alias	name

classGlobalOpts={classopts}

lists options that apply to all classification variables.

Long form	classGlobalOpts={param="BTH" \| "EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHBTH" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"}
Shortcut form	classGlobalOpts="BTH" \| "EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHBTH" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

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

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level for this variable.

Default	FALSE

descending=TRUE | FALSE

when set to True, reverses the sort order that is imposed by the order parameter.

Default	FALSE

ignoreMissing=TRUE | FALSE

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	FALSE

levelizeRaw=TRUE | FALSE

when set to True, bases levelization for this variable on raw values.

Default	FALSE

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

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

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

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

ref="FIRST" | "LAST" | double | "string"

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.

itDetails=TRUE | FALSE

when set to True, requests an iteration history table for smoothing parameters or for regression parameters when fixed smoothing parameters are specified.

Default	FALSE

maxNameLen=integer | "MAX" | "MIN" | "SASV9"

specifies the maximum name length for new columns in any output data table that is created on the CAS server.

Alias	maxNameLength

Default	32
MAX	sets the maximum name length to the maximum integer.
MIN	sets the maximum name length to 20.
SASV9	sets the maximum name length to 32 (which is the maximum valid name length in SAS version 9).

* model={gamplmodel}

names the dependent variable, parametric effects, spline effects, and model options.

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

allObs=TRUE | FALSE

when set to True, constructs a spline by including all observations that do not have a missing value for any variable that is specified in that spline.

Default	FALSE

criterion="DEFAULT" | "GACV" | "GCV" | "UBRE"

specifies the model fitting criterion to optimize.

DEFAULT

uses a model evaluation criterion based on the specified distribution.

GACV

uses the GACV criterion for model evaluation.

GCV

uses the GCV criterion for model evaluation.

UBRE

uses the UBRE criterion for model evaluation.

critFactor=double

adjusts the model fitting criterion for optimization.

Default	1
Minimum value	1

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

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

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

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

descending=TRUE | FALSE

when set to True, reverses the sort order of the response categories. When the descending parameter is set to True and a value is specified for the order parameter, the action orders the response categories according to the requested order and then reverses that order.

Default	FALSE

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

specifies the reference level that is used for the response variable. Valid parameter values are a quoted string that specifies a valid level for the response variable or FIRST or LAST. FIRST and LAST refer to the first and last ordered value of the variable, respectively.

dist="BERNOULLI" | "BINOMIAL" | "GAMMA" | "GAUSSIAN" | "IGAUSSIAN" | "NEGBINOMIAL" | "POISSON" | "TWEEDIE"

specifies the response distribution for the model.

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

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

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

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

specifies the variables to be nested within the term that is defined by the vars parameter. For terms with a BAR or CROSS interaction, the nest corresponds to the last variable in the vars parameter. For terms with no interaction, the nest is distributed across all variables that are listed in the vars parameter.

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

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

fdHessian=TRUE | FALSE

when set to True, uses the finite difference Hessian for optimization.

Default	FALSE

initialPhi=double

specifies the initial dispersion parameter.

Minimum value	0

link="CLOGLOG" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWERMINUS2" | "RECIPROCAL"

specifies the link function for the model.

maxPhi=double

specifies the maximum dispersion parameter.

Minimum value (exclusive)	0

method="OUTER" | "PERFORMANCE"

specifies the model fitting algorithm.

Default	PERFORMANCE

OUTER

uses the outer iteration method to search an optimal model.

PERFORMANCE

uses the performance iteration method to search an optimal model.

minPhi=double

specifies the minimum dispersion parameter.

Minimum value	0

normalize=TRUE | FALSE

when set to True, requests normalized spline basis functions for model fitting.

Default	FALSE

offset="variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

phi=double

specifies the fixed dispersion parameter.

Alias	dispersion
Minimum value	0

ridge=double

specifies the ridge parameter.

Default	0
Range	0–1

scale="DEVIANCE" | "MLE" | "PEARSON"

specifies the fitting method for the dispersion parameter.

Default	MLE

DEVIANCE

uses the deviance statistic to estimate the dispersion parameter.

MLE

uses the maximum likelihood to estimate the dispersion parameter.

PEARSON

uses the Pearson's statistic to estimate the dispersion parameter.

splines={{splineTerm-1} <, {splineTerm-2}, ...>}

names the spline effects to use in the generalized additive models.

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

details_knots=TRUE | FALSE

when set to True, displays knot values for a spline term.

Default	FALSE

details_penalty=TRUE | FALSE

when set to True, displays the penalty matrix for a spline term.

Default	FALSE

details_summary=TRUE | FALSE

when set to True, displays detailed spline construction parameters.

Default	FALSE

df=integer

specifies the fixed degrees of freedom for a spline term.

Minimum value	0

initSmooth=double

specifies the initial smoothing parameter for a spline term.

Minimum value	0

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

specifies a list of knot values to use for a spline term.

m=integer

specifies the order of the derivative in the smoothness penalty for a spline term.

Minimum value	1

maxDf=integer

specifies the maximum degrees of freedom for a spline term.

Minimum value	1

maxKnots=integer

specifies the maximum number of knots for a spline term.

Default	2000
Minimum value	1

maxSmooth=double

specifies the maximum smoothing parameter for a spline term.

Minimum value	0

minSmooth=double

specifies the minimum smoothing parameter for a spline term.

Minimum value	0

smooth=double

specifies the fixed smoothing parameter for a spline term.

Minimum value	0

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

specifies the variables to use in a spline term.

trial="variable-name"

specifies a positive numeric variable that is the number of trials. When you specify a trial variable, the response variable is called the events variable and it must contain the number of positive responses (or events).

tweedieInitialP=double

specifies the initial power parameter for the Tweedie distribution.

Minimum value	1

tweedieOffset=double

specifies a constant to be added to the response for evaluating the quasi-likelihood for the Tweedie distribution.

Minimum value	0

tweedieP=double

specifies the fixed power parameter for the Tweedie distribution.

Minimum value	1

nClassLevelsPrint=integer

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

Minimum value	0

noPrint=TRUE | FALSE

when set to True, suppresses all output.

Default	FALSE

output={gamplOutputStatement}

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

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

alpha=double

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

Range	(0, 1)

* casOut={casouttable}

specifies the settings for an output table.

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

component=TRUE | FALSE

when set to True, requests componentwise statistics such as standard errors and confidence bands for each individual spline term.

Default	FALSE

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

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

into="string"

names the predicted response level. The default name is Into.

intoCutpt=double

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

Default	0.5
Range	(0, 1)

lower="string"

names the lower Bayesian confidence band for the predicted value. The default name is Lower.

Aliases	lclm
Aliases	lowerMean

pearson="string"

names the Pearson chi-square residual. The default name is Pearson.

Aliases	resChi
Aliases	pears

pred="string"

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

Aliases	p
	predicted
	iLink
	mean

resid="string"

names the residual. The default name is Resid.

Aliases	residual
Aliases	r

std="string"

names the standard error of the linear predictor. The default name is Std.

upper="string"

names the upper Bayesian confidence band for the predicted value. The default name is Upper.

Aliases	uclm
Aliases	upperMean

xBeta="string"

names the linear predictor. The default name is Xbeta.

Alias	linp

outputTables={outputTables}

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

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

Alias	displayOut

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

specifies whether to add raw and formatted values of classification variables to the parameter estimates table.

Default	RAW

NONE

does not add raw or formatted values of classification variables to the parameter estimates table.

RAW

adds raw values of classification variables to the parameter estimates table.

RAW_AND_FORMATTED

adds raw and formatted values of classification variables to the parameter estimates table.

plikeOpts={optimspecs}

specifies optimization parameters for maximum (penalized) likelihood estimation.

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	abstol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absftol
Minimum value	0

absFConv_n=integer

specifies the number of successive iterations for which the absolute function difference convergence criterion must be satisfied before the optimization ends.

Alias	absftol_n
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absGConv_n=integer

specifies the number of successive iterations for which the absolute gradient convergence criterion must be satisfied before the optimization ends.

Alias	absgtol_n
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	ftol
Minimum value	0

fConv_n=integer

specifies the number of successive iterations for which the relative function difference convergence criterion must be satisfied before the optimization ends.

Alias	ftol_n
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gConv_n=integer

specifies the number of successive iterations for which the relative gradient convergence criterion must be satisfied before the optimization ends.

Alias	gtol_n
Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Alias	maxfu
Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Alias	maxit
Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Alias	minit
Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

NEWRAP

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

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed at the starting parameter values.

NRRIDG

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

QUANEW

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

TRUREG

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

singChol=double

specifies the singularity criterion in Cholesky decomposition and matrix inversion operations.

Default	1E-12
Range	0–1

singular=double

specifies the singularity criterion in truncated eigendecomposition to determine its convergence.

Default	1E-12
Range	0–1

smoothOpts={optimspecs}

specifies optimization parameters for smoothing parameter optimization.

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	abstol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absftol
Minimum value	0

absFConv_n=integer

specifies the number of successive iterations for which the absolute function difference convergence criterion must be satisfied before the optimization ends.

Alias	absftol_n
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absGConv_n=integer

specifies the number of successive iterations for which the absolute gradient convergence criterion must be satisfied before the optimization ends.

Alias	absgtol_n
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	ftol
Minimum value	0

fConv_n=integer

specifies the number of successive iterations for which the relative function difference convergence criterion must be satisfied before the optimization ends.

Alias	ftol_n
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gConv_n=integer

specifies the number of successive iterations for which the relative gradient convergence criterion must be satisfied before the optimization ends.

Alias	gtol_n
Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Alias	maxfu
Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Alias	maxit
Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Alias	minit
Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

NEWRAP

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

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed at the starting parameter values.

NRRIDG

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

QUANEW

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

TRUREG

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

store={casouttablebasic}

creates a binary table object on the server that contains model fit information so that you can use it later for scoring.

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

Aliases	savemodel
	save
	savestate

* table={castable}

specifies the input data table.

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

weight="variable-name"

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

gampl Action

Fits generalized additive models by penalized likelihood.

Lua Syntax

results, info = s:gam_gampl{

alpha=double,

={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

={{

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string",

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

}, {...}},

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string"

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",

itDetails=true | false,

maxNameLen=integer | "MAX" | "MIN" | "SASV9",

allObs=true | false,

criterion="DEFAULT" | "GACV" | "GCV" | "UBRE",

critFactor=double,

depVars

={{

name="variable-name",

options={modelopts}

}, {...}},

effects

={{

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

maxInteract=integer,

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

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

}, {...}},

fdHessian=true | false,

initialPhi=double,

maxPhi=double,

method="OUTER" | "PERFORMANCE",

minPhi=double,

normalize=true | false,

offset="variable-name",

phi=double,

ridge=double,

scale="DEVIANCE" | "MLE" | "PEARSON",

splines

={{

details_knots=true | false,

details_penalty=true | false,

details_summary=true | false,

df=integer,

initSmooth=double,

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

m=integer,

maxDf=integer,

maxKnots=integer,

maxSmooth=double,

minSmooth=double,

smooth=double,

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

}, {...}},

trial="variable-name",

tweedieInitialP=double,

tweedieOffset=double,

tweedieP=double

nClassLevelsPrint=integer,

noPrint=true | false,

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", ...>}

component=true | false,

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

into="string",

intoCutpt=double,

lower="string",

pearson="string",

pred="string",

resid="string",

std="string",

upper="string",

xBeta="string"

groupByVarsRaw=true | false,

includeAll=true | false,

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

repeated=true | false,

replace=true | false

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

absConv=double,

absFConv=double,

absFConv_n=integer,

absGConv=double,

absGConv_n=integer,

fConv=double,

fConv_n=integer,

gConv=double,

gConv_n=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

seed=64-bit-integer,

singChol=double,

singular=double,

absConv=double,

absFConv=double,

absFConv_n=integer,

absGConv=double,

absGConv_n=integer,

fConv=double,

fConv_n=integer,

gConv=double,

gConv_n=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

caslib="string",

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

label="string",

lifetime=64-bit-integer,

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

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer

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"

}

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 input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after the model is fit.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	creates a binary table object on the server that contains model fit information so that you can use it later for scoring.

Parameter Descriptions

alpha=double

specifies the significance level to use for hypothesis testing of regression parameters and spline effects, and for the construction of all confidence bands.

Default	0.05
Range	(0, 1)

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

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

names the classification variables to use as explanatory variables in the analysis.

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

countMissing=true | false

when set to True, treats missing as a valid level for this variable.

Default	false

descending=true | false

when set to True, reverses the sort order that is imposed by the order parameter.

Default	false

ignoreMissing=true | false

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	false

levelizeRaw=true | false

when set to True, bases levelization for this variable on raw values.

Default	false

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

ref="FIRST" | "LAST" | double | "string"

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

specifies the classification variables.

Alias	name

classGlobalOpts={classopts}

lists options that apply to all classification variables.

Long form	classGlobalOpts={param="BTH" \| "EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHBTH" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"}
Shortcut form	classGlobalOpts="BTH" \| "EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHBTH" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

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

countMissing=true | false

when set to True, treats missing as a valid level for this variable.

Default	false

descending=true | false

when set to True, reverses the sort order that is imposed by the order parameter.

Default	false

ignoreMissing=true | false

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	false

levelizeRaw=true | false

when set to True, bases levelization for this variable on raw values.

Default	false

maxLev=integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

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

param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

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

ref="FIRST" | "LAST" | double | "string"

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.

itDetails=true | false

when set to True, requests an iteration history table for smoothing parameters or for regression parameters when fixed smoothing parameters are specified.

Default	false

maxNameLen=integer | "MAX" | "MIN" | "SASV9"

specifies the maximum name length for new columns in any output data table that is created on the CAS server.

Alias	maxNameLength

Default	32
MAX	sets the maximum name length to the maximum integer.
MIN	sets the maximum name length to 20.
SASV9	sets the maximum name length to 32 (which is the maximum valid name length in SAS version 9).

* model={gamplmodel}

names the dependent variable, parametric effects, spline effects, and model options.

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

allObs=true | false

when set to True, constructs a spline by including all observations that do not have a missing value for any variable that is specified in that spline.

Default	false

criterion="DEFAULT" | "GACV" | "GCV" | "UBRE"

specifies the model fitting criterion to optimize.

DEFAULT

uses a model evaluation criterion based on the specified distribution.

GACV

uses the GACV criterion for model evaluation.

GCV

uses the GCV criterion for model evaluation.

UBRE

uses the UBRE criterion for model evaluation.

critFactor=double

adjusts the model fitting criterion for optimization.

Default	1
Minimum value	1

depVars={{responsevar-1} <, {responsevar-2}, ...>}

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

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

name="variable-name"

names the response variable.

options={modelopts}

specifies a list of parameters for the response variable.

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

descending=true | false

Default	false

event="FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

ref="FIRST" | "LAST" | double | "string"

dist="BERNOULLI" | "BINOMIAL" | "GAMMA" | "GAUSSIAN" | "IGAUSSIAN" | "NEGBINOMIAL" | "POISSON" | "TWEEDIE"

specifies the response distribution for the model.

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

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

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

fdHessian=true | false

when set to True, uses the finite difference Hessian for optimization.

Default	false

initialPhi=double

specifies the initial dispersion parameter.

Minimum value	0

link="CLOGLOG" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWERMINUS2" | "RECIPROCAL"

specifies the link function for the model.

maxPhi=double

specifies the maximum dispersion parameter.

Minimum value (exclusive)	0

method="OUTER" | "PERFORMANCE"

specifies the model fitting algorithm.

Default	PERFORMANCE

OUTER

uses the outer iteration method to search an optimal model.

PERFORMANCE

uses the performance iteration method to search an optimal model.

minPhi=double

specifies the minimum dispersion parameter.

Minimum value	0

normalize=true | false

when set to True, requests normalized spline basis functions for model fitting.

Default	false

offset="variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

phi=double

specifies the fixed dispersion parameter.

Alias	dispersion
Minimum value	0

ridge=double

specifies the ridge parameter.

Default	0
Range	0–1

scale="DEVIANCE" | "MLE" | "PEARSON"

specifies the fitting method for the dispersion parameter.

Default	MLE

DEVIANCE

uses the deviance statistic to estimate the dispersion parameter.

MLE

uses the maximum likelihood to estimate the dispersion parameter.

PEARSON

uses the Pearson's statistic to estimate the dispersion parameter.

splines={{splineTerm-1} <, {splineTerm-2}, ...>}

names the spline effects to use in the generalized additive models.

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

details_knots=true | false

when set to True, displays knot values for a spline term.

Default	false

details_penalty=true | false

when set to True, displays the penalty matrix for a spline term.

Default	false

details_summary=true | false

when set to True, displays detailed spline construction parameters.

Default	false

df=integer

specifies the fixed degrees of freedom for a spline term.

Minimum value	0

initSmooth=double

specifies the initial smoothing parameter for a spline term.

Minimum value	0

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

specifies a list of knot values to use for a spline term.

m=integer

specifies the order of the derivative in the smoothness penalty for a spline term.

Minimum value	1

maxDf=integer

specifies the maximum degrees of freedom for a spline term.

Minimum value	1

maxKnots=integer

specifies the maximum number of knots for a spline term.

Default	2000
Minimum value	1

maxSmooth=double

specifies the maximum smoothing parameter for a spline term.

Minimum value	0

minSmooth=double

specifies the minimum smoothing parameter for a spline term.

Minimum value	0

smooth=double

specifies the fixed smoothing parameter for a spline term.

Minimum value	0

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

specifies the variables to use in a spline term.

trial="variable-name"

tweedieInitialP=double

specifies the initial power parameter for the Tweedie distribution.

Minimum value	1

tweedieOffset=double

specifies a constant to be added to the response for evaluating the quasi-likelihood for the Tweedie distribution.

Minimum value	0

tweedieP=double

specifies the fixed power parameter for the Tweedie distribution.

Minimum value	1

nClassLevelsPrint=integer

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

Minimum value	0

noPrint=true | false

when set to True, suppresses all output.

Default	false

output={gamplOutputStatement}

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

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

alpha=double

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

Range	(0, 1)

* casOut={casouttable}

specifies the settings for an output table.

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

component=true | false

when set to True, requests componentwise statistics such as standard errors and confidence bands for each individual spline term.

Default	false

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

into="string"

names the predicted response level. The default name is Into.

intoCutpt=double

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

Default	0.5
Range	(0, 1)

lower="string"

names the lower Bayesian confidence band for the predicted value. The default name is Lower.

Aliases	lclm
Aliases	lowerMean

pearson="string"

names the Pearson chi-square residual. The default name is Pearson.

Aliases	resChi
Aliases	pears

pred="string"

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

Aliases	p
	predicted
	iLink
	mean

resid="string"

names the residual. The default name is Resid.

Aliases	residual
Aliases	r

std="string"

names the standard error of the linear predictor. The default name is Std.

upper="string"

names the upper Bayesian confidence band for the predicted value. The default name is Upper.

Aliases	uclm
Aliases	upperMean

xBeta="string"

names the linear predictor. The default name is Xbeta.

Alias	linp

outputTables={outputTables}

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

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

Alias	displayOut

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

specifies whether to add raw and formatted values of classification variables to the parameter estimates table.

Default	RAW

NONE

does not add raw or formatted values of classification variables to the parameter estimates table.

RAW

adds raw values of classification variables to the parameter estimates table.

RAW_AND_FORMATTED

adds raw and formatted values of classification variables to the parameter estimates table.

plikeOpts={optimspecs}

specifies optimization parameters for maximum (penalized) likelihood estimation.

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	abstol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absftol
Minimum value	0

absFConv_n=integer

specifies the number of successive iterations for which the absolute function difference convergence criterion must be satisfied before the optimization ends.

Alias	absftol_n
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absGConv_n=integer

specifies the number of successive iterations for which the absolute gradient convergence criterion must be satisfied before the optimization ends.

Alias	absgtol_n
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	ftol
Minimum value	0

fConv_n=integer

specifies the number of successive iterations for which the relative function difference convergence criterion must be satisfied before the optimization ends.

Alias	ftol_n
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gConv_n=integer

specifies the number of successive iterations for which the relative gradient convergence criterion must be satisfied before the optimization ends.

Alias	gtol_n
Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Alias	maxfu
Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Alias	maxit
Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Alias	minit
Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

NEWRAP

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

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed at the starting parameter values.

NRRIDG

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

QUANEW

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

TRUREG

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

singChol=double

specifies the singularity criterion in Cholesky decomposition and matrix inversion operations.

Default	1E-12
Range	0–1

singular=double

specifies the singularity criterion in truncated eigendecomposition to determine its convergence.

Default	1E-12
Range	0–1

smoothOpts={optimspecs}

specifies optimization parameters for smoothing parameter optimization.

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	abstol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absftol
Minimum value	0

absFConv_n=integer

specifies the number of successive iterations for which the absolute function difference convergence criterion must be satisfied before the optimization ends.

Alias	absftol_n
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absGConv_n=integer

specifies the number of successive iterations for which the absolute gradient convergence criterion must be satisfied before the optimization ends.

Alias	absgtol_n
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	ftol
Minimum value	0

fConv_n=integer

specifies the number of successive iterations for which the relative function difference convergence criterion must be satisfied before the optimization ends.

Alias	ftol_n
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gConv_n=integer

specifies the number of successive iterations for which the relative gradient convergence criterion must be satisfied before the optimization ends.

Alias	gtol_n
Minimum value	0

maxFunc=double

specifies the maximum number of function evaluations.

Alias	maxfu
Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Alias	maxit
Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Alias	minit
Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

NEWRAP

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

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed at the starting parameter values.

NRRIDG

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

QUANEW

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

TRUREG

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

store={casouttablebasic}

creates a binary table object on the server that contains model fit information so that you can use it later for scoring.

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

Aliases	savemodel
	save
	savestate

* table={castable}

specifies the input data table.

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

weight="variable-name"

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

gampl Action

Fits generalized additive models by penalized likelihood.

Python Syntax

results=s.gam.gampl(

alpha=double,

=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

=[{

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

"ref":"FIRST" | "LAST" | double | "string",

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

}<, {...}>],

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

"ref":"FIRST" | "LAST" | double | "string"

"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",

itDetails=True | False,

maxNameLen=integer | "MAX" | "MIN" | "SASV9",

"allObs":True | False,

"criterion":"DEFAULT" | "GACV" | "GCV" | "UBRE",

"critFactor":double,

"depVars"

:[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"effects"

:[{

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

"maxInteract":integer,

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

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

}<, {...}>],

"fdHessian":True | False,

"initialPhi":double,

"maxPhi":double,

"method":"OUTER" | "PERFORMANCE",

"minPhi":double,

"normalize":True | False,

"offset":"variable-name",

"phi":double,

"ridge":double,

"scale":"DEVIANCE" | "MLE" | "PEARSON",

"splines"

:[{

"details_knots":True | False,

"details_penalty":True | False,

"details_summary":True | False,

"df":integer,

"initSmooth":double,

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

"m":integer,

"maxDf":integer,

"maxKnots":integer,

"maxSmooth":double,

"minSmooth":double,

"smooth":double,

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

}<, {...}>],

"trial":"variable-name",

"tweedieInitialP":double,

"tweedieOffset":double,

"tweedieP":double

nClassLevelsPrint=integer,

noPrint=True | False,

"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", ...>]

"component":True | False,

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

"into":"string",

"intoCutpt":double,

"lower":"string",

"pearson":"string",

"pred":"string",

"resid":"string",

"std":"string",

"upper":"string",

"xBeta":"string"

"groupByVarsRaw":True | False,

"includeAll":True | False,

"names":["string-1" <, "string-2", ...>] | {"key-1":{casouttable-1} <, "key-2":{casouttable-2}, ...>},

"repeated":True | False,

"replace":True | False

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

"absConv":double,

"absFConv":double,

"absFConv_n":integer,

"absGConv":double,

"absGConv_n":integer,

"fConv":double,

"fConv_n":integer,

"gConv":double,

"gConv_n":integer,

"maxFunc":double,

"maxIter":double,

"maxTime":double,

"minIter":integer,

seed=64-bit-integer,

singChol=double,

singular=double,

"absConv":double,

"absFConv":double,

"absFConv_n":integer,

"absGConv":double,

"absGConv_n":integer,

"fConv":double,

"fConv_n":integer,

"gConv":double,

"gConv_n":integer,

"maxFunc":double,

"maxIter":double,

"maxTime":double,

"minIter":integer,

"caslib":"string",

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

"label":"string",

"lifetime":64-bit-integer,

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

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer

"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 | 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 | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

"name":"table-name"

"vars"

:[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

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 input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after the model is fit.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
store	—	creates a binary table object on the server that contains model fit information so that you can use it later for scoring.

Parameter Descriptions

alpha=double

specifies the significance level to use for hypothesis testing of regression parameters and spline effects, and for the construction of all confidence bands.

Default	0.05
Range	(0, 1)

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

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

names the classification variables to use as explanatory variables in the analysis.

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

"countMissing":True | False

when set to True, treats missing as a valid level for this variable.

Default	False

"descending":True | False

when set to True, reverses the sort order that is imposed by the order parameter.

Default	False

"ignoreMissing":True | False

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	False

"levelizeRaw":True | False

when set to True, bases levelization for this variable on raw values.

Default	False

"maxLev":integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

"ref":"FIRST" | "LAST" | double | "string"

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

specifies the classification variables.

Alias	name

classGlobalOpts={classopts}

lists options that apply to all classification variables.

Long form	classGlobalOpts={"param":"BTH" \| "EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHBTH" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"}
Shortcut form	classGlobalOpts="BTH" \| "EFFECT" \| "GLM" \| "ORDINAL" \| "ORTHBTH" \| "ORTHEFFECT" \| "ORTHORDINAL" \| "ORTHPOLY" \| "ORTHREF" \| "POLYNOMIAL" \| "REFERENCE"

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

"countMissing":True | False

when set to True, treats missing as a valid level for this variable.

Default	False

"descending":True | False

when set to True, reverses the sort order that is imposed by the order parameter.

Default	False

"ignoreMissing":True | False

when set to True, ignores the fact that some variables in the observation have missing values and honors the nonmissing values for other variables in that observation.

Default	False

"levelizeRaw":True | False

when set to True, bases levelization for this variable on raw values.

Default	False

"maxLev":integer

specifies the maximum number of levels. A value of 0 means an unlimited number of levels.

Default	0
Minimum value	0

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the classification variable. This ordering determines which parameters in the model correspond to each level in the data.

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

"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE"

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

"ref":"FIRST" | "LAST" | double | "string"

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.

itDetails=True | False

when set to True, requests an iteration history table for smoothing parameters or for regression parameters when fixed smoothing parameters are specified.

Default	False

maxNameLen=integer | "MAX" | "MIN" | "SASV9"

specifies the maximum name length for new columns in any output data table that is created on the CAS server.

Alias	maxNameLength

Default	32
MAX	sets the maximum name length to the maximum integer.
MIN	sets the maximum name length to 20.
SASV9	sets the maximum name length to 32 (which is the maximum valid name length in SAS version 9).

* model={gamplmodel}

names the dependent variable, parametric effects, spline effects, and model options.

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

"allObs":True | False

when set to True, constructs a spline by including all observations that do not have a missing value for any variable that is specified in that spline.

Default	False

"criterion":"DEFAULT" | "GACV" | "GCV" | "UBRE"

specifies the model fitting criterion to optimize.

DEFAULT

uses a model evaluation criterion based on the specified distribution.

GACV

uses the GACV criterion for model evaluation.

GCV

uses the GCV criterion for model evaluation.

UBRE

uses the UBRE criterion for model evaluation.

"critFactor":double

adjusts the model fitting criterion for optimization.

Default	1
Minimum value	1

"depVars":[{responsevar-1} <, {responsevar-2}, ...>]

specifies one or more variables to use as response variables in the model. Not all models support more than one response variable.

Aliases	depVar
Aliases	target

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

"name":"variable-name"

names the response variable.

"options":{modelopts}

specifies a list of parameters for the response variable.

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

"descending":True | False

Default	False

"event":"FIRST" | "LAST" | double | "string"

specifies the event category for the binary response model. FIRST and LAST refer to the first and last ordered value of the response, respectively.

"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL"

specifies the sort order for the levels of the response variable. This ordering determines which parameters in the model correspond to each level in the data.

"ref":"FIRST" | "LAST" | double | "string"

"dist":"BERNOULLI" | "BINOMIAL" | "GAMMA" | "GAUSSIAN" | "IGAUSSIAN" | "NEGBINOMIAL" | "POISSON" | "TWEEDIE"

specifies the response distribution for the model.

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

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

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

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

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

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

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

"fdHessian":True | False

when set to True, uses the finite difference Hessian for optimization.

Default	False

"initialPhi":double

specifies the initial dispersion parameter.

Minimum value	0

"link":"CLOGLOG" | "IDENTITY" | "LOG" | "LOGIT" | "LOGLOG" | "NORMIT" | "POWERMINUS2" | "RECIPROCAL"

specifies the link function for the model.

"maxPhi":double

specifies the maximum dispersion parameter.

Minimum value (exclusive)	0

"method":"OUTER" | "PERFORMANCE"

specifies the model fitting algorithm.

Default	PERFORMANCE

OUTER

uses the outer iteration method to search an optimal model.

PERFORMANCE

uses the performance iteration method to search an optimal model.

"minPhi":double

specifies the minimum dispersion parameter.

Minimum value	0

"normalize":True | False

when set to True, requests normalized spline basis functions for model fitting.

Default	False

"offset":"variable-name"

specifies a numeric offset variable. This variable cannot be a classification variable, a response variable, or one of the explanatory variables.

"phi":double

specifies the fixed dispersion parameter.

Alias	dispersion
Minimum value	0

"ridge":double

specifies the ridge parameter.

Default	0
Range	0–1

"scale":"DEVIANCE" | "MLE" | "PEARSON"

specifies the fitting method for the dispersion parameter.

Default	MLE

DEVIANCE

uses the deviance statistic to estimate the dispersion parameter.

MLE

uses the maximum likelihood to estimate the dispersion parameter.

PEARSON

uses the Pearson's statistic to estimate the dispersion parameter.

"splines":[{splineTerm-1} <, {splineTerm-2}, ...>]

names the spline effects to use in the generalized additive models.

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

"details_knots":True | False

when set to True, displays knot values for a spline term.

Default	False

"details_penalty":True | False

when set to True, displays the penalty matrix for a spline term.

Default	False

"details_summary":True | False

when set to True, displays detailed spline construction parameters.

Default	False

"df":integer

specifies the fixed degrees of freedom for a spline term.

Minimum value	0

"initSmooth":double

specifies the initial smoothing parameter for a spline term.

Minimum value	0

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

specifies a list of knot values to use for a spline term.

"m":integer

specifies the order of the derivative in the smoothness penalty for a spline term.

Minimum value	1

"maxDf":integer

specifies the maximum degrees of freedom for a spline term.

Minimum value	1

"maxKnots":integer

specifies the maximum number of knots for a spline term.

Default	2000
Minimum value	1

"maxSmooth":double

specifies the maximum smoothing parameter for a spline term.

Minimum value	0

"minSmooth":double

specifies the minimum smoothing parameter for a spline term.

Minimum value	0

"smooth":double

specifies the fixed smoothing parameter for a spline term.

Minimum value	0

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

specifies the variables to use in a spline term.

"trial":"variable-name"

"tweedieInitialP":double

specifies the initial power parameter for the Tweedie distribution.

Minimum value	1

"tweedieOffset":double

specifies a constant to be added to the response for evaluating the quasi-likelihood for the Tweedie distribution.

Minimum value	0

"tweedieP":double

specifies the fixed power parameter for the Tweedie distribution.

Minimum value	1

nClassLevelsPrint=integer

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

Minimum value	0

noPrint=True | False

when set to True, suppresses all output.

Default	False

output={gamplOutputStatement}

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

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

"alpha":double

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

Range	(0, 1)

* "casOut":{casouttable}

specifies the settings for an output table.

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

"component":True | False

when set to True, requests componentwise statistics such as standard errors and confidence bands for each individual spline term.

Default	False

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

"into":"string"

names the predicted response level. The default name is Into.

"intoCutpt":double

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

Default	0.5
Range	(0, 1)

"lower":"string"

names the lower Bayesian confidence band for the predicted value. The default name is Lower.

Aliases	lclm
Aliases	lowerMean

"pearson":"string"

names the Pearson chi-square residual. The default name is Pearson.

Aliases	resChi
Aliases	pears

"pred":"string"

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

Aliases	p
	predicted
	iLink
	mean

"resid":"string"

names the residual. The default name is Resid.

Aliases	residual
Aliases	r

"std":"string"

names the standard error of the linear predictor. The default name is Std.

"upper":"string"

names the upper Bayesian confidence band for the predicted value. The default name is Upper.

Aliases	uclm
Aliases	upperMean

"xBeta":"string"

names the linear predictor. The default name is Xbeta.

Alias	linp

outputTables={outputTables}

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

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

Alias	displayOut

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

specifies whether to add raw and formatted values of classification variables to the parameter estimates table.

Default	RAW

NONE

does not add raw or formatted values of classification variables to the parameter estimates table.

RAW

adds raw values of classification variables to the parameter estimates table.

RAW_AND_FORMATTED

adds raw and formatted values of classification variables to the parameter estimates table.

plikeOpts={optimspecs}

specifies optimization parameters for maximum (penalized) likelihood estimation.

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

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

"absConv":double

specifies the absolute function convergence criterion.

Alias	abstol

"absFConv":double

specifies the absolute function difference convergence criterion.

Alias	absftol
Minimum value	0

"absFConv_n":integer

specifies the number of successive iterations for which the absolute function difference convergence criterion must be satisfied before the optimization ends.

Alias	absftol_n
Minimum value	0

"absGConv":double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

"absGConv_n":integer

specifies the number of successive iterations for which the absolute gradient convergence criterion must be satisfied before the optimization ends.

Alias	absgtol_n
Minimum value	0

"fConv":double

specifies the relative function difference convergence criterion.

Alias	ftol
Minimum value	0

"fConv_n":integer

specifies the number of successive iterations for which the relative function difference convergence criterion must be satisfied before the optimization ends.

Alias	ftol_n
Minimum value	0

"gConv":double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

"gConv_n":integer

specifies the number of successive iterations for which the relative gradient convergence criterion must be satisfied before the optimization ends.

Alias	gtol_n
Minimum value	0

"maxFunc":double

specifies the maximum number of function evaluations.

Alias	maxfu
Minimum value	0

"maxIter":double

specifies the maximum number of iterations.

Alias	maxit
Minimum value	0

"maxTime":double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

"minIter":integer

specifies the minimum number of iterations.

Alias	minit
Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

NEWRAP

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

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed at the starting parameter values.

NRRIDG

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

QUANEW

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

TRUREG

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

seed=64-bit-integer

specifies a seed for starting the pseudorandom number generator.

Default	0
Range	0–4294967295

singChol=double

specifies the singularity criterion in Cholesky decomposition and matrix inversion operations.

Default	1E-12
Range	0–1

singular=double

specifies the singularity criterion in truncated eigendecomposition to determine its convergence.

Default	1E-12
Range	0–1

smoothOpts={optimspecs}

specifies optimization parameters for smoothing parameter optimization.

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

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

"absConv":double

specifies the absolute function convergence criterion.

Alias	abstol

"absFConv":double

specifies the absolute function difference convergence criterion.

Alias	absftol
Minimum value	0

"absFConv_n":integer

specifies the number of successive iterations for which the absolute function difference convergence criterion must be satisfied before the optimization ends.

Alias	absftol_n
Minimum value	0

"absGConv":double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

"absGConv_n":integer

specifies the number of successive iterations for which the absolute gradient convergence criterion must be satisfied before the optimization ends.

Alias	absgtol_n
Minimum value	0

"fConv":double

specifies the relative function difference convergence criterion.

Alias	ftol
Minimum value	0

"fConv_n":integer

specifies the number of successive iterations for which the relative function difference convergence criterion must be satisfied before the optimization ends.

Alias	ftol_n
Minimum value	0

"gConv":double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

"gConv_n":integer

specifies the number of successive iterations for which the relative gradient convergence criterion must be satisfied before the optimization ends.

Alias	gtol_n
Minimum value	0

"maxFunc":double

specifies the maximum number of function evaluations.

Alias	maxfu
Minimum value	0

"maxIter":double

specifies the maximum number of iterations.

Alias	maxit
Minimum value	0

"maxTime":double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

"minIter":integer

specifies the minimum number of iterations.

Alias	minit
Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

NEWRAP

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

NMSIMP

uses the Nelder-Mead simplex method, which does not require any derivatives.

NONE

does not perform any optimization. Results are computed at the starting parameter values.

NRRIDG

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

QUANEW

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

TRUREG

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

store={casouttablebasic}

creates a binary table object on the server that contains model fit information so that you can use it later for scoring.

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

Aliases	savemodel
	save
	savestate

* table={castable}

specifies the input data table.

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

weight="variable-name"

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

gampl Action

Fits generalized additive models by penalized likelihood.

R Syntax

results <– cas.gam.gampl(s,

alpha=double,

=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

=list( list(

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string",

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

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

=list(

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",

ref="FIRST" | "LAST" | double | "string"

=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",

itDetails=TRUE | FALSE,

maxNameLen=integer | "MAX" | "MIN" | "SASV9",

=list(

allObs=TRUE | FALSE,

criterion="DEFAULT" | "GACV" | "GCV" | "UBRE",

critFactor=double,

depVars

=list( list(

name="variable-name",

options=list(modelopts)

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

effects

=list( list(

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

maxInteract=integer,

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

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

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

fdHessian=TRUE | FALSE,

initialPhi=double,

maxPhi=double,

method="OUTER" | "PERFORMANCE",

minPhi=double,

normalize=TRUE | FALSE,

offset="variable-name",

phi=double,

ridge=double,

scale="DEVIANCE" | "MLE" | "PEARSON",

splines

=list( list(

details_knots=TRUE | FALSE,

details_penalty=TRUE | FALSE,

details_summary=TRUE | FALSE,

df=integer,

initSmooth=double,

knots=integer | list(double-1 <, double-2, ...>),

m=integer,

maxDf=integer,

maxKnots=integer,

maxSmooth=double,

minSmooth=double,

smooth=double,

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

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

trial="variable-name",

tweedieInitialP=double,

tweedieOffset=double,

tweedieP=double

nClassLevelsPrint=integer,

noPrint=TRUE | FALSE,

=list(

alpha=double,

casOut

=list(

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

component=TRUE | FALSE,

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

into="string",

intoCutpt=double,

lower="string",

pearson="string",

pred="string",

resid="string",

std="string",

upper="string",

xBeta="string"

=list(

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

names=list("string-1" <, "string-2", ...>) | list(key-1=list(casouttable-1) <, key-2=list(casouttable-2), ...>),

repeated=TRUE | FALSE,

replace=TRUE | FALSE

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

=list(

absConv=double,

absFConv=double,

absFConv_n=integer,

absGConv=double,

absGConv_n=integer,

fConv=double,

fConv_n=integer,

gConv=double,

gConv_n=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

seed=64-bit-integer,

singChol=double,

singular=double,

=list(

absConv=double,

absFConv=double,

absFConv_n=integer,

absGConv=double,

absGConv_n=integer,

fConv=double,

fConv_n=integer,

gConv=double,

gConv_n=integer,

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

=list(

caslib="string",

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

label="string",

lifetime=64-bit-integer,

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

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer