Nonlinear Models Action Set: Syntax

Provides actions for fitting nonlinear models

nlmod Action

Fits nonlinear regression models using either nonlinear least squares or maximum likelihood.

CASL Syntax

nonlinear.nlmod <result=results> <status=rc> /

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

bounds={{

lb=double,

leb=double,

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

ub=double,

ueb=double

}, {...}},

display={

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

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

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

estimate={{

alpha=double,

df=double,

expression="string",

label="string"

}, {...}},

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

model={

depvar="string",

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

nlmodCode="string",

nlmodOptions={

alpha=double,

corr=TRUE | FALSE,

cov=TRUE | FALSE,

df=double,

eCorr=TRUE | FALSE,

eCov=TRUE | FALSE,

noItPrint=TRUE | FALSE,

noPrint=TRUE | FALSE

optimizeOpts={

absconv=double,

absfconv=double,

absfconvN=64-bit-integer,

absgconv=double,

absgconvN=64-bit-integer,

fconv=double,

fconvN=64-bit-integer,

gconv=double,

gconvN=64-bit-integer,

maxfunc=64-bit-integer,

maxiter=64-bit-integer,

maxtime=double,

miniter=64-bit-integer,

outputTables={

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

parameters={{

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

vals=double | {double-1 <, double-2, ...>} | {parmvalftb}

}, {...}},

parmData={

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 | sforce-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"

}

parmOptions={

best=64-bit-integer,

start=double

predict={{

alpha=double,

df=double,

expression="string",

label="string",

lower="string",

pred="string",

probt="string",

stderr="string",

tvalue="string",

upper="string"

}, {...}},

predOut={

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", ...>}

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

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"

}

tolerenceOpts={

singular=double

}

;

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
parmData	—	specifies the data table that provides parameter starting values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
outputTables	names	lists the names of results tables to save as CAS tables on the server.
predOut	—	specifies the output data table.

Parameter Descriptions

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

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

bounds={{bounds_Statement-1} <, {bounds_Statement-2}, ...>}

lists the bounds for the parameters in the model.

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

lb=double

specifies an exclusive lower bound value for the named parameter (that is, the parameter must be greater than this value).

leb=double

specifies an inclusive lower bound value with an equality sign for the named parameter (that is, the parameter is greater than or equal to this value).

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

specifies the name of the parameter that needs to be restricted by a bound.

ub=double

specifies an exclusive upper bound value for the named parameter (that is, the parameter must be less than this value).

ueb=double

specifies an inclusive upper bound value with an equality sign for the named parameter (that is, the parameter is less than or equal to this value).

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).

estimate={{estimate_Statement-1} <, {estimate_Statement-2}, ...>}

lists the additional expressions that need to be estimated after the model is fitted.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for additional estimates.

Range	0–1

df=double

specifies the degrees of freedom to use in finding p-values for additional estimates.

* expression="string"

specifies the expression to be estimated.

* label="string"

specifies the label for the estimated expression.

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

lists the variables that need to be stored in an output data table.

model={model_Statement}

specifies the dependent variable, its distribution, and its distribution parameters.

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

* depvar="string"

specifies the dependent variable.

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

specifies the dependent variable distribution parameters.

* distribution="BERNOULLI" | "BINOMIAL" | "GAM" | "GAUSS" | "GENERAL" | "LS" | "NB" | "POISSON" | "T"

specifies the type of distribution for the dependent variable.

BERNOULLI

specifies binary distribution.

Alias	BINARY

BINOMIAL

specifies binomial distribution.

GAMDIST

specifies gamma distribution.

Aliases	GAM
GAMMA

GAUSSIAN

specifies normal distribution.

Aliases	N
GAUSS
NORMAL

GENERAL

specifies general distribution.

RESIDUAL

specifies least squares or residual type.

Alias	LS

NEGBIN

specifies negative binomial distribution.

Alias	NB

POISSON

specifies Poisson distribution.

TDIST

specifies T distribution.

Alias	T

nlmodCode="string"

specifies the nonlinear programming statements in a quoted string.

nlmodOptions={nlmod_options}

lists options that uses in NLMOD action.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for additional parameter estimates.

Default	0.05
Range	0–1

corr=TRUE | FALSE

when set to True, displays the approximate correlation matrix for the parameter estimates.

Default	FALSE

cov=TRUE | FALSE

when set to True, displays the approximate covariance matrix for the parameter estimates.

Default	FALSE

df=double

specifies the degrees of freedom to use in calculating p-values for additional parameter estimates.

Minimum value	0

eCorr=TRUE | FALSE

when set to True, displays the approximate correlation matrix for all expressions that are specified in the estimate parameter.

Default	FALSE

eCov=TRUE | FALSE

when set to True, displays the approximate covariance matrix for all expressions that are specified in the estimate parameter.

Default	FALSE

noItPrint=TRUE | FALSE

when set to True, suppresses the display of the Iteration History table.

Default	FALSE

noPrint=TRUE | FALSE

when set to True, suppresses output

Default	FALSE

optimizeOpts={opti_options}

specifies optimization-related options.

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

absconv=double

specifies the absolute function convergence criterion.

Alias	abstol

absfconv=double

specifies the absolute difference function convergence criterion.

Alias	absftol
Minimum value	0

absfconvN=64-bit-integer

specifies the number of additional iterations for which the absolute difference function convergence criterion must be satisfied before the process terminates.

Alias	absftolN
Default	0
Minimum value	0

absgconv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absgconvN=64-bit-integer

specifies the number of additional iterations for which the absolute gradient convergence criterion must be satisfied before the process terminates.

Alias	absgtolN
Default	0
Minimum value	0

fconv=double

specifies the relative function convergence criterion.

Alias	ftol
Minimum value	0

fconvN=64-bit-integer

specifies the number of additional iterations for which the difference function convergence criterion must be satisfied before the process terminates.

Alias	ftolN
Default	0
Minimum value	0

gconv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gconvN=64-bit-integer

specifies the number of additional iterations for which the gradient convergence criterion must be satisfied before the process terminates.

Alias	gtolN
Default	0
Minimum value	0

maxfunc=64-bit-integer

specifies the maximum number of function evaluations in any optimization.

Alias	maxfu
Minimum value	0

maxiter=64-bit-integer

specifies the maximum number of iterations in any optimization.

Alias	maxit
Minimum value	1

maxtime=double

specifies the upper limit (in seconds) of CPU time for any optimization.

Minimum value	0

miniter=64-bit-integer

specifies the minimum number of iterations in any optimization.

Default	-1
Minimum value	0

technique="CG" | "DBLDOG" | "DQN" | "LEVMAR" | "NEWRAP" | "NMS" | "NONE" | "NRR" | "QN" | "TR"

specifies the optimization technique.

CONGRA

specifies conjugate gradient optimization.

Alias	CG

DBLDOG

specifies double-dogleg optimization.

Alias	DD

DUQUANEW

specifies dual quasi-Newton optimization.

Alias	DQN

LEVMAR

specifies Levenberg-Marquardt nonlinear least squares optimization.

Aliases	LM
LS

NEWRAP

specifies Newton Raphson optimization.

Alias	NRA

NMSIMP

specifies Nelder-Mead simple optimization.

Alias	NMS

NONE

specifies no optimization.

NRRIDG

specifies Newton Raphson ridge optimization.

Alias	NRR

QUANEW

specifies quasi-Newton optimization.

Alias	QN

TRUREG

specifies trust region optimization.

Alias	TR

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

parameters={{parms_Statement-1} <, {parms_Statement-2}, ...>}

lists the parameters of the model and their initial values.

Alias	parms

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

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

specifies the parameter name.

vals=double | {double-1 <, double-2, ...>} | {parmvalftb}

specifies the initial values for the parameter.

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

by=double

specifies the BY value in a FROM BY TO format of an initial values specification.

Default	1

* from=double

specifies the FROM value in a FROM BY TO format of an initial values specification.

* to=double

specifies the TO value in a FROM BY TO format of an initial values specification.

parmData={castable}

specifies the data table that provides parameter starting values.

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

parmOptions={parm_options}

specifies the options for the initial parameter specifications.

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

best=64-bit-integer

specifies the maximum number of initial values to display.

Minimum value	1

start=double

specifies a default initial value for all the parameters in the model.

Alias	defstart

predict={{predict_Statement-1} <, {predict_Statement-2}, ...>}

lists the expressions that need to be predicted after the model is fitted.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for an individual prediction.

Range	0–1

df=double

specifies the degrees of freedom to use in finding p-values for an individual prediction.

* expression="string"

specifies the expression to be predicted.

* label="string"

specifies the label for predicted expression.

lower="string"

names the lower bound of a confidence interval for an individual prediction.

pred="string"

names the predicted value for an individual prediction.

probt="string"

names the p-value for an individual prediction.

stderr="string"

names the standard error for an individual prediction.

tvalue="string"

names the t value for an individual prediction.

upper="string"

names the upper bound of a confidence interval for an individual prediction.

predOut={casouttable}

specifies the output data table.

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

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

specifies the linear restriction for the model.

* table={castable}

specifies the input data table.

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

tolerenceOpts={toln_options}

specifies tolerance-related options.

singular=double

specifies the general singularity criterion.

Range	0–1

nlmod Action

Fits nonlinear regression models using either nonlinear least squares or maximum likelihood.

Lua Syntax

results, info = s:nonlinear_nlmod{

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

bounds={{

lb=double,

leb=double,

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

ub=double,

ueb=double

}, {...}},

display={

caseSensitive=true | false,

exclude=true | false,

excludeAll=true | false,

keyIsPath=true | false,

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

pathType="LABEL" | "NAME",

traceNames=true | false

estimate={{

alpha=double,

df=double,

expression="string",

label="string"

}, {...}},

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

model={

depvar="string",

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

nlmodCode="string",

nlmodOptions={

alpha=double,

corr=true | false,

cov=true | false,

df=double,

eCorr=true | false,

eCov=true | false,

noItPrint=true | false,

noPrint=true | false

optimizeOpts={

absconv=double,

absfconv=double,

absfconvN=64-bit-integer,

absgconv=double,

absgconvN=64-bit-integer,

fconv=double,

fconvN=64-bit-integer,

gconv=double,

gconvN=64-bit-integer,

maxfunc=64-bit-integer,

maxiter=64-bit-integer,

maxtime=double,

miniter=64-bit-integer,

outputTables={

groupByVarsRaw=true | false,

includeAll=true | false,

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

repeated=true | false,

replace=true | false

parameters={{

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

vals=double | {double-1 <, double-2, ...>} | {parmvalftb}

}, {...}},

parmData={

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"

}

parmOptions={

best=64-bit-integer,

start=double

predict={{

alpha=double,

df=double,

expression="string",

label="string",

lower="string",

pred="string",

probt="string",

stderr="string",

tvalue="string",

upper="string"

}, {...}},

predOut={

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", ...>}

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

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"

}

tolerenceOpts={

singular=double

}

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
parmData	—	specifies the data table that provides parameter starting values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
outputTables	names	lists the names of results tables to save as CAS tables on the server.
predOut	—	specifies the output data table.

Parameter Descriptions

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

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

bounds={{bounds_Statement-1} <, {bounds_Statement-2}, ...>}

lists the bounds for the parameters in the model.

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

lb=double

specifies an exclusive lower bound value for the named parameter (that is, the parameter must be greater than this value).

leb=double

specifies an inclusive lower bound value with an equality sign for the named parameter (that is, the parameter is greater than or equal to this value).

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

specifies the name of the parameter that needs to be restricted by a bound.

ub=double

specifies an exclusive upper bound value for the named parameter (that is, the parameter must be less than this value).

ueb=double

specifies an inclusive upper bound value with an equality sign for the named parameter (that is, the parameter is less than or equal to this value).

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).

estimate={{estimate_Statement-1} <, {estimate_Statement-2}, ...>}

lists the additional expressions that need to be estimated after the model is fitted.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for additional estimates.

Range	0–1

df=double

specifies the degrees of freedom to use in finding p-values for additional estimates.

* expression="string"

specifies the expression to be estimated.

* label="string"

specifies the label for the estimated expression.

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

lists the variables that need to be stored in an output data table.

model={model_Statement}

specifies the dependent variable, its distribution, and its distribution parameters.

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

* depvar="string"

specifies the dependent variable.

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

specifies the dependent variable distribution parameters.

* distribution="BERNOULLI" | "BINOMIAL" | "GAM" | "GAUSS" | "GENERAL" | "LS" | "NB" | "POISSON" | "T"

specifies the type of distribution for the dependent variable.

BERNOULLI

specifies binary distribution.

Alias	BINARY

BINOMIAL

specifies binomial distribution.

GAMDIST

specifies gamma distribution.

Aliases	GAM
GAMMA

GAUSSIAN

specifies normal distribution.

Aliases	N
GAUSS
NORMAL

GENERAL

specifies general distribution.

RESIDUAL

specifies least squares or residual type.

Alias	LS

NEGBIN

specifies negative binomial distribution.

Alias	NB

POISSON

specifies Poisson distribution.

TDIST

specifies T distribution.

Alias	T

nlmodCode="string"

specifies the nonlinear programming statements in a quoted string.

nlmodOptions={nlmod_options}

lists options that uses in NLMOD action.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for additional parameter estimates.

Default	0.05
Range	0–1

corr=true | false

when set to True, displays the approximate correlation matrix for the parameter estimates.

Default	false

cov=true | false

when set to True, displays the approximate covariance matrix for the parameter estimates.

Default	false

df=double

specifies the degrees of freedom to use in calculating p-values for additional parameter estimates.

Minimum value	0

eCorr=true | false

when set to True, displays the approximate correlation matrix for all expressions that are specified in the estimate parameter.

Default	false

eCov=true | false

when set to True, displays the approximate covariance matrix for all expressions that are specified in the estimate parameter.

Default	false

noItPrint=true | false

when set to True, suppresses the display of the Iteration History table.

Default	false

noPrint=true | false

when set to True, suppresses output

Default	false

optimizeOpts={opti_options}

specifies optimization-related options.

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

absconv=double

specifies the absolute function convergence criterion.

Alias	abstol

absfconv=double

specifies the absolute difference function convergence criterion.

Alias	absftol
Minimum value	0

absfconvN=64-bit-integer

specifies the number of additional iterations for which the absolute difference function convergence criterion must be satisfied before the process terminates.

Alias	absftolN
Default	0
Minimum value	0

absgconv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absgconvN=64-bit-integer

specifies the number of additional iterations for which the absolute gradient convergence criterion must be satisfied before the process terminates.

Alias	absgtolN
Default	0
Minimum value	0

fconv=double

specifies the relative function convergence criterion.

Alias	ftol
Minimum value	0

fconvN=64-bit-integer

specifies the number of additional iterations for which the difference function convergence criterion must be satisfied before the process terminates.

Alias	ftolN
Default	0
Minimum value	0

gconv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gconvN=64-bit-integer

specifies the number of additional iterations for which the gradient convergence criterion must be satisfied before the process terminates.

Alias	gtolN
Default	0
Minimum value	0

maxfunc=64-bit-integer

specifies the maximum number of function evaluations in any optimization.

Alias	maxfu
Minimum value	0

maxiter=64-bit-integer

specifies the maximum number of iterations in any optimization.

Alias	maxit
Minimum value	1

maxtime=double

specifies the upper limit (in seconds) of CPU time for any optimization.

Minimum value	0

miniter=64-bit-integer

specifies the minimum number of iterations in any optimization.

Default	-1
Minimum value	0

technique="CG" | "DBLDOG" | "DQN" | "LEVMAR" | "NEWRAP" | "NMS" | "NONE" | "NRR" | "QN" | "TR"

specifies the optimization technique.

CONGRA

specifies conjugate gradient optimization.

Alias	CG

DBLDOG

specifies double-dogleg optimization.

Alias	DD

DUQUANEW

specifies dual quasi-Newton optimization.

Alias	DQN

LEVMAR

specifies Levenberg-Marquardt nonlinear least squares optimization.

Aliases	LM
LS

NEWRAP

specifies Newton Raphson optimization.

Alias	NRA

NMSIMP

specifies Nelder-Mead simple optimization.

Alias	NMS

NONE

specifies no optimization.

NRRIDG

specifies Newton Raphson ridge optimization.

Alias	NRR

QUANEW

specifies quasi-Newton optimization.

Alias	QN

TRUREG

specifies trust region optimization.

Alias	TR

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

parameters={{parms_Statement-1} <, {parms_Statement-2}, ...>}

lists the parameters of the model and their initial values.

Alias	parms

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

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

specifies the parameter name.

vals=double | {double-1 <, double-2, ...>} | {parmvalftb}

specifies the initial values for the parameter.

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

by=double

specifies the BY value in a FROM BY TO format of an initial values specification.

Default	1

* from=double

specifies the FROM value in a FROM BY TO format of an initial values specification.

* to=double

specifies the TO value in a FROM BY TO format of an initial values specification.

parmData={castable}

specifies the data table that provides parameter starting values.

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

parmOptions={parm_options}

specifies the options for the initial parameter specifications.

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

best=64-bit-integer

specifies the maximum number of initial values to display.

Minimum value	1

start=double

specifies a default initial value for all the parameters in the model.

Alias	defstart

predict={{predict_Statement-1} <, {predict_Statement-2}, ...>}

lists the expressions that need to be predicted after the model is fitted.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for an individual prediction.

Range	0–1

df=double

specifies the degrees of freedom to use in finding p-values for an individual prediction.

* expression="string"

specifies the expression to be predicted.

* label="string"

specifies the label for predicted expression.

lower="string"

names the lower bound of a confidence interval for an individual prediction.

pred="string"

names the predicted value for an individual prediction.

probt="string"

names the p-value for an individual prediction.

stderr="string"

names the standard error for an individual prediction.

tvalue="string"

names the t value for an individual prediction.

upper="string"

names the upper bound of a confidence interval for an individual prediction.

predOut={casouttable}

specifies the output data table.

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

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

specifies the linear restriction for the model.

* table={castable}

specifies the input data table.

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

tolerenceOpts={toln_options}

specifies tolerance-related options.

singular=double

specifies the general singularity criterion.

Range	0–1

nlmod Action

Fits nonlinear regression models using either nonlinear least squares or maximum likelihood.

Python Syntax

results= s.nonlinear.nlmod(

attributes=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

bounds=[{

"lb":double,

"leb":double,

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

"ub":double,

"ueb":double

}<, {...}>],

display={

"caseSensitive":True | False,

"exclude":True | False,

"excludeAll":True | False,

"keyIsPath":True | False,

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

"pathType":"LABEL" | "NAME",

"traceNames":True | False

estimate=[{

"alpha":double,

"df":double,

"expression":"string",

"label":"string"

}<, {...}>],

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

model={

"depvar":"string",

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

nlmodCode="string",

nlmodOptions={

"alpha":double,

"corr":True | False,

"cov":True | False,

"df":double,

"eCorr":True | False,

"eCov":True | False,

"noItPrint":True | False,

"noPrint":True | False

optimizeOpts={

"absconv":double,

"absfconv":double,

"absfconvN":64-bit-integer,

"absgconv":double,

"absgconvN":64-bit-integer,

"fconv":double,

"fconvN":64-bit-integer,

"gconv":double,

"gconvN":64-bit-integer,

"maxfunc":64-bit-integer,

"maxiter":64-bit-integer,

"maxtime":double,

"miniter":64-bit-integer,

outputTables={

"groupByVarsRaw":True | False,

"includeAll":True | False,

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

"repeated":True | False,

"replace":True | False

parameters=[{

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

"vals":double | [double-1 <, double-2, ...>] | {parmvalftb}

}<, {...}>],

parmData={

"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 | sforce-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"

}

parmOptions={

"best":64-bit-integer,

"start":double

predict=[{

"alpha":double,

"df":double,

"expression":"string",

"label":"string",

"lower":"string",

"pred":"string",

"probt":"string",

"stderr":"string",

"tvalue":"string",

"upper":"string"

}<, {...}>],

predOut={

"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", ...>]

restrict=["string-1" <, "string-2", ...>],

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"

}

tolerenceOpts={

"singular":double

}

)

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
parmData	—	specifies the data table that provides parameter starting values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
outputTables	names	lists the names of results tables to save as CAS tables on the server.
predOut	—	specifies the output data table.

Parameter Descriptions

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

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

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

lists the bounds for the parameters in the model.

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

"lb":double

specifies an exclusive lower bound value for the named parameter (that is, the parameter must be greater than this value).

"leb":double

specifies an inclusive lower bound value with an equality sign for the named parameter (that is, the parameter is greater than or equal to this value).

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

specifies the name of the parameter that needs to be restricted by a bound.

"ub":double

specifies an exclusive upper bound value for the named parameter (that is, the parameter must be less than this value).

"ueb":double

specifies an inclusive upper bound value with an equality sign for the named parameter (that is, the parameter is less than or equal to this value).

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).

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

lists the additional expressions that need to be estimated after the model is fitted.

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

"alpha":double

specifies the alpha value to use in constructing confidence intervals for additional estimates.

Range	0–1

"df":double

specifies the degrees of freedom to use in finding p-values for additional estimates.

* "expression":"string"

specifies the expression to be estimated.

* "label":"string"

specifies the label for the estimated expression.

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

lists the variables that need to be stored in an output data table.

model={model_Statement}

specifies the dependent variable, its distribution, and its distribution parameters.

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

* "depvar":"string"

specifies the dependent variable.

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

specifies the dependent variable distribution parameters.

* "distribution":"BERNOULLI" | "BINOMIAL" | "GAM" | "GAUSS" | "GENERAL" | "LS" | "NB" | "POISSON" | "T"

specifies the type of distribution for the dependent variable.

BERNOULLI

specifies binary distribution.

Alias	BINARY

BINOMIAL

specifies binomial distribution.

GAMDIST

specifies gamma distribution.

Aliases	GAM
GAMMA

GAUSSIAN

specifies normal distribution.

Aliases	N
GAUSS
NORMAL

GENERAL

specifies general distribution.

RESIDUAL

specifies least squares or residual type.

Alias	LS

NEGBIN

specifies negative binomial distribution.

Alias	NB

POISSON

specifies Poisson distribution.

TDIST

specifies T distribution.

Alias	T

nlmodCode="string"

specifies the nonlinear programming statements in a quoted string.

nlmodOptions={nlmod_options}

lists options that uses in NLMOD action.

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

"alpha":double

specifies the alpha value to use in constructing confidence intervals for additional parameter estimates.

Default	0.05
Range	0–1

"corr":True | False

when set to True, displays the approximate correlation matrix for the parameter estimates.

Default	False

"cov":True | False

when set to True, displays the approximate covariance matrix for the parameter estimates.

Default	False

"df":double

specifies the degrees of freedom to use in calculating p-values for additional parameter estimates.

Minimum value	0

"eCorr":True | False

when set to True, displays the approximate correlation matrix for all expressions that are specified in the estimate parameter.

Default	False

"eCov":True | False

when set to True, displays the approximate covariance matrix for all expressions that are specified in the estimate parameter.

Default	False

"noItPrint":True | False

when set to True, suppresses the display of the Iteration History table.

Default	False

"noPrint":True | False

when set to True, suppresses output

Default	False

optimizeOpts={opti_options}

specifies optimization-related options.

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

"absconv":double

specifies the absolute function convergence criterion.

Alias	abstol

"absfconv":double

specifies the absolute difference function convergence criterion.

Alias	absftol
Minimum value	0

"absfconvN":64-bit-integer

specifies the number of additional iterations for which the absolute difference function convergence criterion must be satisfied before the process terminates.

Alias	absftolN
Default	0
Minimum value	0

"absgconv":double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

"absgconvN":64-bit-integer

specifies the number of additional iterations for which the absolute gradient convergence criterion must be satisfied before the process terminates.

Alias	absgtolN
Default	0
Minimum value	0

"fconv":double

specifies the relative function convergence criterion.

Alias	ftol
Minimum value	0

"fconvN":64-bit-integer

specifies the number of additional iterations for which the difference function convergence criterion must be satisfied before the process terminates.

Alias	ftolN
Default	0
Minimum value	0

"gconv":double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

"gconvN":64-bit-integer

specifies the number of additional iterations for which the gradient convergence criterion must be satisfied before the process terminates.

Alias	gtolN
Default	0
Minimum value	0

"maxfunc":64-bit-integer

specifies the maximum number of function evaluations in any optimization.

Alias	maxfu
Minimum value	0

"maxiter":64-bit-integer

specifies the maximum number of iterations in any optimization.

Alias	maxit
Minimum value	1

"maxtime":double

specifies the upper limit (in seconds) of CPU time for any optimization.

Minimum value	0

"miniter":64-bit-integer

specifies the minimum number of iterations in any optimization.

Default	-1
Minimum value	0

"technique":"CG" | "DBLDOG" | "DQN" | "LEVMAR" | "NEWRAP" | "NMS" | "NONE" | "NRR" | "QN" | "TR"

specifies the optimization technique.

CONGRA

specifies conjugate gradient optimization.

Alias	CG

DBLDOG

specifies double-dogleg optimization.

Alias	DD

DUQUANEW

specifies dual quasi-Newton optimization.

Alias	DQN

LEVMAR

specifies Levenberg-Marquardt nonlinear least squares optimization.

Aliases	LM
LS

NEWRAP

specifies Newton Raphson optimization.

Alias	NRA

NMSIMP

specifies Nelder-Mead simple optimization.

Alias	NMS

NONE

specifies no optimization.

NRRIDG

specifies Newton Raphson ridge optimization.

Alias	NRR

QUANEW

specifies quasi-Newton optimization.

Alias	QN

TRUREG

specifies trust region optimization.

Alias	TR

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

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

lists the parameters of the model and their initial values.

Alias	parms

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

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

specifies the parameter name.

"vals":double | [double-1 <, double-2, ...>] | {parmvalftb}

specifies the initial values for the parameter.

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

"by":double

specifies the BY value in a FROM BY TO format of an initial values specification.

Default	1

* "from_":double

specifies the FROM value in a FROM BY TO format of an initial values specification.

* "to":double

specifies the TO value in a FROM BY TO format of an initial values specification.

parmData={castable}

specifies the data table that provides parameter starting values.

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

parmOptions={parm_options}

specifies the options for the initial parameter specifications.

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

"best":64-bit-integer

specifies the maximum number of initial values to display.

Minimum value	1

"start":double

specifies a default initial value for all the parameters in the model.

Alias	defstart

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

lists the expressions that need to be predicted after the model is fitted.

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

"alpha":double

specifies the alpha value to use in constructing confidence intervals for an individual prediction.

Range	0–1

"df":double

specifies the degrees of freedom to use in finding p-values for an individual prediction.

* "expression":"string"

specifies the expression to be predicted.

* "label":"string"

specifies the label for predicted expression.

"lower":"string"

names the lower bound of a confidence interval for an individual prediction.

"pred":"string"

names the predicted value for an individual prediction.

"probt":"string"

names the p-value for an individual prediction.

"stderr":"string"

names the standard error for an individual prediction.

"tvalue":"string"

names the t value for an individual prediction.

"upper":"string"

names the upper bound of a confidence interval for an individual prediction.

predOut={casouttable}

specifies the output data table.

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

restrict=["string-1" <, "string-2", ...>]

specifies the linear restriction for the model.

* table={castable}

specifies the input data table.

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

tolerenceOpts={toln_options}

specifies tolerance-related options.

"singular":double

specifies the general singularity criterion.

Range	0–1

nlmod Action

Fits nonlinear regression models using either nonlinear least squares or maximum likelihood.

R Syntax

results <– cas.nonlinear.nlmod(s,

attributes=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

bounds=list( list(

lb=double,

leb=double,

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

ub=double,

ueb=double

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

display=list(

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

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

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

estimate=list( list(

alpha=double,

df=double,

expression="string",

label="string"

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

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

model=list(

depvar="string",

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

nlmodCode="string",

nlmodOptions=list(

alpha=double,

corr=TRUE | FALSE,

cov=TRUE | FALSE,

df=double,

eCorr=TRUE | FALSE,

eCov=TRUE | FALSE,

noItPrint=TRUE | FALSE,

noPrint=TRUE | FALSE

optimizeOpts=list(

absconv=double,

absfconv=double,

absfconvN=64-bit-integer,

absgconv=double,

absgconvN=64-bit-integer,

fconv=double,

fconvN=64-bit-integer,

gconv=double,

gconvN=64-bit-integer,

maxfunc=64-bit-integer,

maxiter=64-bit-integer,

maxtime=double,

miniter=64-bit-integer,

outputTables=list(

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

parameters=list( list(

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

vals=double | list(double-1 <, double-2, ...>) | list(parmvalftb)

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

parmData=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"

)

parmOptions=list(

best=64-bit-integer,

start=double

predict=list( list(

alpha=double,

df=double,

expression="string",

label="string",

lower="string",

pred="string",

probt="string",

stderr="string",

tvalue="string",

upper="string"

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

predOut=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", ...>)

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

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"

)

tolerenceOpts=list(

singular=double

)

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
parmData	—	specifies the data table that provides parameter starting values.
required parametertable	—	specifies the input data table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
outputTables	names	lists the names of results tables to save as CAS tables on the server.
predOut	—	specifies the output data table.

Parameter Descriptions

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

changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored.

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

Alias	attribute

bounds=list( list(bounds_Statement-1) <, list(bounds_Statement-2), ...>)

lists the bounds for the parameters in the model.

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

lb=double

specifies an exclusive lower bound value for the named parameter (that is, the parameter must be greater than this value).

leb=double

specifies an inclusive lower bound value with an equality sign for the named parameter (that is, the parameter is greater than or equal to this value).

* name="string" | list("string-1" <, "string-2", ...>)

specifies the name of the parameter that needs to be restricted by a bound.

ub=double

specifies an exclusive upper bound value for the named parameter (that is, the parameter must be less than this value).

ueb=double

specifies an inclusive upper bound value with an equality sign for the named parameter (that is, the parameter is less than or equal to this value).

display=list(displayTables)

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

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

estimate=list( list(estimate_Statement-1) <, list(estimate_Statement-2), ...>)

lists the additional expressions that need to be estimated after the model is fitted.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for additional estimates.

Range	0–1

df=double

specifies the degrees of freedom to use in finding p-values for additional estimates.

* expression="string"

specifies the expression to be estimated.

* label="string"

specifies the label for the estimated expression.

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

lists the variables that need to be stored in an output data table.

model=list(model_Statement)

specifies the dependent variable, its distribution, and its distribution parameters.

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

* depvar="string"

specifies the dependent variable.

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

specifies the dependent variable distribution parameters.

* distribution="BERNOULLI" | "BINOMIAL" | "GAM" | "GAUSS" | "GENERAL" | "LS" | "NB" | "POISSON" | "T"

specifies the type of distribution for the dependent variable.

BERNOULLI

specifies binary distribution.

Alias	BINARY

BINOMIAL

specifies binomial distribution.

GAMDIST

specifies gamma distribution.

Aliases	GAM
GAMMA

GAUSSIAN

specifies normal distribution.

Aliases	N
GAUSS
NORMAL

GENERAL

specifies general distribution.

RESIDUAL

specifies least squares or residual type.

Alias	LS

NEGBIN

specifies negative binomial distribution.

Alias	NB

POISSON

specifies Poisson distribution.

TDIST

specifies T distribution.

Alias	T

nlmodCode="string"

specifies the nonlinear programming statements in a quoted string.

nlmodOptions=list(nlmod_options)

lists options that uses in NLMOD action.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for additional parameter estimates.

Default	0.05
Range	0–1

corr=TRUE | FALSE

when set to True, displays the approximate correlation matrix for the parameter estimates.

Default	FALSE

cov=TRUE | FALSE

when set to True, displays the approximate covariance matrix for the parameter estimates.

Default	FALSE

df=double

specifies the degrees of freedom to use in calculating p-values for additional parameter estimates.

Minimum value	0

eCorr=TRUE | FALSE

when set to True, displays the approximate correlation matrix for all expressions that are specified in the estimate parameter.

Default	FALSE

eCov=TRUE | FALSE

when set to True, displays the approximate covariance matrix for all expressions that are specified in the estimate parameter.

Default	FALSE

noItPrint=TRUE | FALSE

when set to True, suppresses the display of the Iteration History table.

Default	FALSE

noPrint=TRUE | FALSE

when set to True, suppresses output

Default	FALSE

optimizeOpts=list(opti_options)

specifies optimization-related options.

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

absconv=double

specifies the absolute function convergence criterion.

Alias	abstol

absfconv=double

specifies the absolute difference function convergence criterion.

Alias	absftol
Minimum value	0

absfconvN=64-bit-integer

specifies the number of additional iterations for which the absolute difference function convergence criterion must be satisfied before the process terminates.

Alias	absftolN
Default	0
Minimum value	0

absgconv=double

specifies the absolute gradient convergence criterion.

Alias	absgtol
Minimum value	0

absgconvN=64-bit-integer

specifies the number of additional iterations for which the absolute gradient convergence criterion must be satisfied before the process terminates.

Alias	absgtolN
Default	0
Minimum value	0

fconv=double

specifies the relative function convergence criterion.

Alias	ftol
Minimum value	0

fconvN=64-bit-integer

specifies the number of additional iterations for which the difference function convergence criterion must be satisfied before the process terminates.

Alias	ftolN
Default	0
Minimum value	0

gconv=double

specifies the relative gradient convergence criterion.

Alias	gtol
Minimum value	0

gconvN=64-bit-integer

specifies the number of additional iterations for which the gradient convergence criterion must be satisfied before the process terminates.

Alias	gtolN
Default	0
Minimum value	0

maxfunc=64-bit-integer

specifies the maximum number of function evaluations in any optimization.

Alias	maxfu
Minimum value	0

maxiter=64-bit-integer

specifies the maximum number of iterations in any optimization.

Alias	maxit
Minimum value	1

maxtime=double

specifies the upper limit (in seconds) of CPU time for any optimization.

Minimum value	0

miniter=64-bit-integer

specifies the minimum number of iterations in any optimization.

Default	-1
Minimum value	0

technique="CG" | "DBLDOG" | "DQN" | "LEVMAR" | "NEWRAP" | "NMS" | "NONE" | "NRR" | "QN" | "TR"

specifies the optimization technique.

CONGRA

specifies conjugate gradient optimization.

Alias	CG

DBLDOG

specifies double-dogleg optimization.

Alias	DD

DUQUANEW

specifies dual quasi-Newton optimization.

Alias	DQN

LEVMAR

specifies Levenberg-Marquardt nonlinear least squares optimization.

Aliases	LM
LS

NEWRAP

specifies Newton Raphson optimization.

Alias	NRA

NMSIMP

specifies Nelder-Mead simple optimization.

Alias	NMS

NONE

specifies no optimization.

NRRIDG

specifies Newton Raphson ridge optimization.

Alias	NRR

QUANEW

specifies quasi-Newton optimization.

Alias	QN

TRUREG

specifies trust region optimization.

Alias	TR

outputTables=list(outputTables)

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

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

Alias	displayOut

parameters=list( list(parms_Statement-1) <, list(parms_Statement-2), ...>)

lists the parameters of the model and their initial values.

Alias	parms

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

* name="string" | list("string-1" <, "string-2", ...>)

specifies the parameter name.

vals=double | list(double-1 <, double-2, ...>) | {parmvalftb}

specifies the initial values for the parameter.

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

by=double

specifies the BY value in a FROM BY TO format of an initial values specification.

Default	1

* from=double

specifies the FROM value in a FROM BY TO format of an initial values specification.

* to=double

specifies the TO value in a FROM BY TO format of an initial values specification.

parmData=list(castable)

specifies the data table that provides parameter starting values.

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

parmOptions=list(parm_options)

specifies the options for the initial parameter specifications.

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

best=64-bit-integer

specifies the maximum number of initial values to display.

Minimum value	1

start=double

specifies a default initial value for all the parameters in the model.

Alias	defstart

predict=list( list(predict_Statement-1) <, list(predict_Statement-2), ...>)

lists the expressions that need to be predicted after the model is fitted.

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

alpha=double

specifies the alpha value to use in constructing confidence intervals for an individual prediction.

Range	0–1

df=double

specifies the degrees of freedom to use in finding p-values for an individual prediction.

* expression="string"

specifies the expression to be predicted.

* label="string"

specifies the label for predicted expression.

lower="string"

names the lower bound of a confidence interval for an individual prediction.

pred="string"

names the predicted value for an individual prediction.

probt="string"

names the p-value for an individual prediction.

stderr="string"

names the standard error for an individual prediction.

tvalue="string"

names the t value for an individual prediction.

upper="string"

names the upper bound of a confidence interval for an individual prediction.

predOut=list(casouttable)

specifies the output data table.

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

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

specifies the linear restriction for the model.

* 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).

tolerenceOpts=list(toln_options)

specifies tolerance-related options.

singular=double

specifies the general singularity criterion.

Range	0–1

Last updated: September 09, 2021