Quantile Regression Modeling Action Set: Syntax

Provides actions for performing quantile regression

quantreg Action

Fits quantile regression models.

CASL Syntax

quantreg.quantreg <result=results> <status=rc> /

alpha=double,

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

byLimit=64-bit-integer,

class={{

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

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

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

split=TRUE | FALSE,

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

}, {...}},

classLevelsPrint=TRUE | FALSE,

classglobalopts={

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

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

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

split=TRUE | FALSE

code={

casOut={

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=TRUE | FALSE,

fmtWdth=integer,

iProb=TRUE | FALSE,

indentSize=integer,

intoCutPt=double,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

pCatAll=TRUE | FALSE,

tabForm=TRUE | FALSE

collection={{

details=TRUE | FALSE,

name="string",

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

}, {...}},

cov={

sparsity={

BF=TRUE | FALSE

HS=TRUE | FALSE

}

display={

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

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

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

hidestopstep=TRUE | FALSE,

maxParameters=integer,

model={

clb=TRUE | FALSE,

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

effects={{

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

maxInteract=integer,

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

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

}, {...}},

entry="variable-name",

include=integer | {{effect-1} <, {effect-2}, ...>},

informative=TRUE | FALSE,

noint=TRUE | FALSE,

nquantlevs=integer,

quantiles={double-1 <, double-2, ...>},

quantsort=TRUE | FALSE,

start=integer | {{effect-1} <, {effect-2}, ...>},

stb=TRUE | FALSE

multimember={{

details=TRUE | FALSE,

name="string",

noEffect=TRUE | FALSE,

stdize=TRUE | FALSE,

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

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

}, {...}},

nClassLevelsPrint=integer,

output={

LCLM="string",

STDP="string",

UCLM="string",

casOut={

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

pred="string",

resid="string",

role="string"

outputTables={

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

partbyfrac={

seed=integer,

test=double,

validate=double

partbyvar={

name="variable-name",

test="string",

train="string",

validate="string"

polynomial={{

degree=integer,

details=TRUE | FALSE,

labelStyle={

expand=TRUE | FALSE

exponent="string"

includeName=TRUE | FALSE

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=TRUE | FALSE,

standardize={

method="MOMENTS" | "MRANGE" | "WMOMENTS"

options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

prefix="NONE" | "string"

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

}, {...}},

selection={

L2=double,

L2HIGH=double,

L2LOW=double,

candidates=integer | "ALL",

competitive=TRUE | FALSE,

details="ALL" | "NONE" | "STEPS" | "SUMMARY",

enscale=TRUE | FALSE,

ensteps=integer,

gamma=double,

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

maxEffects=integer,

maxSteps=integer,

minEffects=integer,

plots=TRUE | FALSE,

select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",

slEntry=double,

slStay=double,

stopHorizon=integer

spline={{

basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

dataBoundary=TRUE | FALSE,

degree=integer,

details=TRUE | FALSE,

knotMax=double,

knotMethod={

equal=integer

list={double-1 <, double-2, ...>}

listWithBoundary={double-1 <, double-2, ...>}

multiscale={

endScale=integer

startScale=integer

}

rangeFractions={double-1 <, double-2, ...>}

knotMin=double,

name="string",

naturalCubic=TRUE | FALSE,

separate=TRUE | FALSE,

split=TRUE | FALSE,

vars={"variable-name-1" <, "variable-name-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",

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

name="table-name",

onDemand=TRUE | FALSE,

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

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

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

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

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

byLimit=64-bit-integer

specifies that the analysis not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

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

names the classification variables to be used as explanatory variables in the analysis.

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

Alias	classVars, nominal

classLevelsPrint=TRUE | FALSE

when set to False, suppresses the display of class levels.

Default	TRUE

classglobalopts={classopts}

lists options that apply to all classification variables.

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

code={aircodegen}

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

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

collection={{collection-1} <, {collection-2}, ...>}

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

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

details=TRUE | FALSE

when set to True, requests a table that shows additional details that are related to this effect.

Default	FALSE

* name="string"

specifies the name of the effect.

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

specifies a set of variables that are treated as a single effect that has multiple degrees of freedom. The columns in the design matrix that are contributed by a collection effect are the design columns of its constituent variables in the order in which they appear in the definition of the collection effect.

cov={qrscov}

specifies the method and parameters for covariance estimation.

sparsity={qrssparsity}

specifies the parameters for sparsity estimation.

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

BF=TRUE | FALSE

when set to True, specifies the Bofinger method of sparsity estimation.

Default	FALSE

HS=TRUE | FALSE

when set to True, specifies the Hall-Sheather method of sparsity estimation.

Default	FALSE

display={displayTables}

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

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

hidestopstep=TRUE | FALSE

when set to True, hides the stopping step in the selection summary table. This parameter also forces the model selection process to ignore the stopping step in choosing the final model.

Alias	hidestopsteps
Default	FALSE

maxParameters=integer

specifies that models not be fit if the number of parameters exceeds the specified value.

Minimum value	0

model={qrsmodel}

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

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

clb=TRUE | FALSE

when set to True, displays upper and lower confidence limits for the parameter estimates.

Default	FALSE

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

specifies a list of variables to use as response variables in the model.

Alias	depVar, 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" | "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.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

specifies the type of the response variable. The types NOMINAL, ORDINAL and BINARY specify that the response variable should be levelized. When the value of this parameter is INTERVAL, all other parameters specified for this response variable are ignored and the response variable is not levelized.

Default	INTERVAL

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" | "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.

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.

entry="variable-name"

specifies the entry variable.

include=integer | {{effect-1} <, {effect-2}, ...>}

specifies effects to include at the start of the selection process in the FORWARD, FORWARDSWAP, and STEPWISE selection methods. Included effects are never dropped during the selection process. If you specify n, where n is a positive integer, then the included effects consist of the first n effects of the model specification.

The effect value is specified as follows:

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.

informative=TRUE | FALSE

when set to True, models missing values by using extra model effects. These effects consist of dummy variables that take the value 1 when the value of a continuous model variable involved in the effect is missing, and take the value 0 otherwise. The missing value in the original model effect is replaced by the average value of the effect for the nonmissing values. For classification variables, missing values are treated as valid levels.

Default	FALSE

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

nquantlevs=integer

specifies the number of quantile levels to be equally spaced in (0,1).

Alias	nquantlev, nqlevs, ntaus, nqlev, ntau, nq
Minimum value	1

quantiles={double-1 <, double-2, ...>}

specifies the quantile levels.

Alias	quantile, qlevs, qlev, q, quantlev

quantsort=TRUE | FALSE

when set to True, sorts all the specified quantile levels in ascending order. This parameter also removes redundant and invalid quantile levels.

Alias	qsort, qtsort, qst
Default	FALSE

start=integer | {{effect-1} <, {effect-2}, ...>}

specifies effects to use to begin the selection process in the FORWARD, FORWARDSWAP, and STEPWISE selection methods. If you specify n, where n is a positive integer, then the starting model consists of the first n effects of the model specification.

The effect value is specified as follows:

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.

stb=TRUE | FALSE

when set to True, produces standardized regression coefficients. A standardized regression coefficient is computed by dividing a parameter estimate by the ratio of the sample standard deviation of the dependent variable to the sample standard deviation of the regressor.

Default	FALSE

multimember={{multimember-1} <, {multimember-2}, ...>}

uses one or more classification variables specified in the vars parameter in such a way that each observation can be associated with one or more levels of the union of the levels of the classification variables.

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

nClassLevelsPrint=integer

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

Minimum value	0

output={qrsoutputStatement}

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

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

LCLM="string"

names the lower bounds of confidence intervals for the predicted quantiles.

STDP="string"

names the standard error of the predicted quantiles.

UCLM="string"

names the upper bound of a confidence interval for predicted quantiles.

* 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).

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.

pred="string"

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

resid="string"

names the residual, calculated as ACTUAL minus PREDICTED.

role="string"

identifies the training, validation, and test roles for the observations.

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

partbyfrac={partByFracStatement}

specifies the fractions of the data to be used for validation and testing.

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

seed=integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

test=double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

validate=double

randomly assigns the specified proportion of observations in the input table to the validation role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Alias	valid
Range	0–1

partbyvar={partByVarStatement}

names the variable and its values used to partition the data into training, validation, and testing roles.

Long form	partbyvar={name="variable-name"}
Shortcut form	partbyvar="variable-name"

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

* name="variable-name"

names the variable in the input table whose values are used to assign rows to each observation.

test="string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

train="string"

specifies the formatted value of the variable that is used to assign observations to the training role. If you do not specify the train parameter, then all observations whose roles are not determined by the test and validate parameters are assigned to training.

validate="string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

polynomial={{polynomial-1} <, {polynomial-2}, ...>}

specifies a polynomial effect. All specified variables must be numeric. A design matrix column is generated for each term of the specified polynomial. By default, each of these terms is treated as a separate effect for the purpose of model building.

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

Alias	poly

selection={selectionStatement}

specifies the method and options for performing model selection.

Long form	selection={method="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"

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

L2=double

specifies the L2 in elastic net method

Default	0

L2HIGH=double

specifies the highest value used in the search of L2 in elastic net method

Default	1

L2LOW=double

specifies the lowest value used in the search of L2 in elastic net method

Default	0

candidates=integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

choose="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

competitive=TRUE | FALSE

when set to True, evaluates (during stepwise selection) the selection criterion for all models in which an effect currently in the model is dropped or an effect not yet in the model is added. The effect whose removal from or addition to the model yields the maximum improvement to the selection criterion is dropped or added.

Default	FALSE

details="ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

Default	SUMMARY

enscale=TRUE | FALSE

when set to True, applies scale to beta in elastic net method

Default	FALSE

ensteps=integer

specifies the number of iteration in elastic net method

Default	50

gamma=double

specifies the exponent of the power transformation that is applied to the parameters in forming the adaptive weights.

Default	1

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

specifies whether and how to apply the model hierarchy requirement. Model hierarchy refers to the requirement that, for any term to be in the model, all model effects that are contained in the term must be present in the model.

Default	DEFAULT

maxEffects=integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

maxSteps=integer

specifies the maximum number of selection steps to perform.

method="BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE"

specifies the model selection method.

Default	STEPWISE

minEffects=integer

specifies the minimum number of effects in any model to consider during backward selection.

plots=TRUE | FALSE

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

Default	FALSE

select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

slEntry=double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

slStay=double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

stop="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

stopHorizon=integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

Default	3

spline={{spline-1} <, {spline-2}, ...>}

expands variables into spline bases whose form depends on the specified parameters.

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

* 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.

quantreg Action

Fits quantile regression models.

Lua Syntax

results, info = s:quantreg_quantreg{

alpha=double,

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

byLimit=64-bit-integer,

class={{

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

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

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

split=true | false,

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

}, {...}},

classLevelsPrint=true | false,

classglobalopts={

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

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

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

split=true | false

code={

casOut={

caslib="string"

compress=true | false

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=true | false,

fmtWdth=integer,

iProb=true | false,

indentSize=integer,

intoCutPt=double,

labelId=integer,

lineSize=integer,

noTrim=true | false,

pCatAll=true | false,

tabForm=true | false

collection={{

details=true | false,

name="string",

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

}, {...}},

cov={

sparsity={

BF=true | false

HS=true | false

}

display={

caseSensitive=true | false,

exclude=true | false,

excludeAll=true | false,

keyIsPath=true | false,

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

pathType="LABEL" | "NAME",

traceNames=true | false

hidestopstep=true | false,

maxParameters=integer,

model={

clb=true | false,

depVars={{

name="variable-name",

options={modelopts}

}, {...}},

effects={{

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

maxInteract=integer,

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

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

}, {...}},

entry="variable-name",

include=integer | {{effect-1} <, {effect-2}, ...>},

informative=true | false,

noint=true | false,

nquantlevs=integer,

quantiles={double-1 <, double-2, ...>},

quantsort=true | false,

start=integer | {{effect-1} <, {effect-2}, ...>},

stb=true | false

multimember={{

details=true | false,

name="string",

noEffect=true | false,

stdize=true | false,

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

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

}, {...}},

nClassLevelsPrint=integer,

output={

LCLM="string",

STDP="string",

UCLM="string",

casOut={

caslib="string"

compress=true | false

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

pred="string",

resid="string",

role="string"

outputTables={

groupByVarsRaw=true | false,

includeAll=true | false,

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

repeated=true | false,

replace=true | false

partbyfrac={

seed=integer,

test=double,

validate=double

partbyvar={

name="variable-name",

test="string",

train="string",

validate="string"

polynomial={{

degree=integer,

details=true | false,

labelStyle={

expand=true | false

exponent="string"

includeName=true | false

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=true | false,

standardize={

method="MOMENTS" | "MRANGE" | "WMOMENTS"

options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

prefix="NONE" | "string"

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

}, {...}},

selection={

L2=double,

L2HIGH=double,

L2LOW=double,

candidates=integer | "ALL",

competitive=true | false,

details="ALL" | "NONE" | "STEPS" | "SUMMARY",

enscale=true | false,

ensteps=integer,

gamma=double,

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

maxEffects=integer,

maxSteps=integer,

minEffects=integer,

plots=true | false,

select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",

slEntry=double,

slStay=double,

stopHorizon=integer

spline={{

basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

dataBoundary=true | false,

degree=integer,

details=true | false,

knotMax=double,

knotMethod={

equal=integer

list={double-1 <, double-2, ...>}

listWithBoundary={double-1 <, double-2, ...>}

multiscale={

endScale=integer

startScale=integer

}

rangeFractions={double-1 <, double-2, ...>}

knotMin=double,

name="string",

naturalCubic=true | false,

separate=true | false,

split=true | false,

vars={"variable-name-1" <, "variable-name-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"

}

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

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

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

byLimit=64-bit-integer

specifies that the analysis not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

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

names the classification variables to be used as explanatory variables in the analysis.

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

Alias	classVars, nominal

classLevelsPrint=true | false

when set to False, suppresses the display of class levels.

Default	true

classglobalopts={classopts}

lists options that apply to all classification variables.

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

code={aircodegen}

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

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

collection={{collection-1} <, {collection-2}, ...>}

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

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

details=true | false

when set to True, requests a table that shows additional details that are related to this effect.

Default	false

* name="string"

specifies the name of the effect.

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

cov={qrscov}

specifies the method and parameters for covariance estimation.

sparsity={qrssparsity}

specifies the parameters for sparsity estimation.

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

BF=true | false

when set to True, specifies the Bofinger method of sparsity estimation.

Default	false

HS=true | false

when set to True, specifies the Hall-Sheather method of sparsity estimation.

Default	false

display={displayTables}

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

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

hidestopstep=true | false

when set to True, hides the stopping step in the selection summary table. This parameter also forces the model selection process to ignore the stopping step in choosing the final model.

Alias	hidestopsteps
Default	false

maxParameters=integer

specifies that models not be fit if the number of parameters exceeds the specified value.

Minimum value	0

model={qrsmodel}

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

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

clb=true | false

when set to True, displays upper and lower confidence limits for the parameter estimates.

Default	false

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

specifies a list of variables to use as response variables in the model.

Alias	depVar, 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" | "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.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

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" | "string"

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.

entry="variable-name"

specifies the entry variable.

include=integer | {{effect-1} <, {effect-2}, ...>}

The effect value is specified as follows:

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.

informative=true | false

Default	false

noint=true | false

when set to True, does not include the intercept term in the model.

Default	false

nquantlevs=integer

specifies the number of quantile levels to be equally spaced in (0,1).

Alias	nquantlev, nqlevs, ntaus, nqlev, ntau, nq
Minimum value	1

quantiles={double-1 <, double-2, ...>}

specifies the quantile levels.

Alias	quantile, qlevs, qlev, q, quantlev

quantsort=true | false

when set to True, sorts all the specified quantile levels in ascending order. This parameter also removes redundant and invalid quantile levels.

Alias	qsort, qtsort, qst
Default	false

start=integer | {{effect-1} <, {effect-2}, ...>}

The effect value is specified as follows:

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.

stb=true | false

Default	false

multimember={{multimember-1} <, {multimember-2}, ...>}

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

nClassLevelsPrint=integer

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

Minimum value	0

output={qrsoutputStatement}

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

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

LCLM="string"

names the lower bounds of confidence intervals for the predicted quantiles.

STDP="string"

names the standard error of the predicted quantiles.

UCLM="string"

names the upper bound of a confidence interval for predicted quantiles.

* 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).

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

pred="string"

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

resid="string"

names the residual, calculated as ACTUAL minus PREDICTED.

role="string"

identifies the training, validation, and test roles for the observations.

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

partbyfrac={partByFracStatement}

specifies the fractions of the data to be used for validation and testing.

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

seed=integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

test=double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

validate=double

Alias	valid
Range	0–1

partbyvar={partByVarStatement}

names the variable and its values used to partition the data into training, validation, and testing roles.

Long form	partbyvar={name="variable-name"}
Shortcut form	partbyvar="variable-name"

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

* name="variable-name"

names the variable in the input table whose values are used to assign rows to each observation.

test="string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

train="string"

validate="string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

polynomial={{polynomial-1} <, {polynomial-2}, ...>}

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

Alias	poly

selection={selectionStatement}

specifies the method and options for performing model selection.

Long form	selection={method="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"

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

L2=double

specifies the L2 in elastic net method

Default	0

L2HIGH=double

specifies the highest value used in the search of L2 in elastic net method

Default	1

L2LOW=double

specifies the lowest value used in the search of L2 in elastic net method

Default	0

candidates=integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

choose="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

competitive=true | false

Default	false

details="ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

Default	SUMMARY

enscale=true | false

when set to True, applies scale to beta in elastic net method

Default	false

ensteps=integer

specifies the number of iteration in elastic net method

Default	50

gamma=double

specifies the exponent of the power transformation that is applied to the parameters in forming the adaptive weights.

Default	1

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

Default	DEFAULT

maxEffects=integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

maxSteps=integer

specifies the maximum number of selection steps to perform.

method="BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE"

specifies the model selection method.

Default	STEPWISE

minEffects=integer

specifies the minimum number of effects in any model to consider during backward selection.

plots=true | false

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

Default	false

select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

slEntry=double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

slStay=double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

stop="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

stopHorizon=integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

Default	3

spline={{spline-1} <, {spline-2}, ...>}

expands variables into spline bases whose form depends on the specified parameters.

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

* 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.

quantreg Action

Fits quantile regression models.

Python Syntax

results= s.quantreg.quantreg(

alpha=double,

attributes=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

byLimit=64-bit-integer,

class_=[{

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

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

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

"split":True | False,

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

}<, {...}>],

classLevelsPrint=True | False,

classglobalopts={

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

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

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

"split":True | False

code={

"casOut":{

"caslib":"string"

"compress":True | False

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

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

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

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

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

"comment":True | False,

"fmtWdth":integer,

"iProb":True | False,

"indentSize":integer,

"intoCutPt":double,

"labelId":integer,

"lineSize":integer,

"noTrim":True | False,

"pCatAll":True | False,

"tabForm":True | False

collection=[{

"details":True | False,

"name":"string",

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

}<, {...}>],

cov={

"sparsity":{

"BF":True | False

"HS":True | False

}

display={

"caseSensitive":True | False,

"exclude":True | False,

"excludeAll":True | False,

"keyIsPath":True | False,

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

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

"traceNames":True | False

hidestopstep=True | False,

maxParameters=integer,

model={

"clb":True | False,

"depVars":[{

"name":"variable-name",

"options":{modelopts}

}<, {...}>],

"effects":[{

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

"maxInteract":integer,

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

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

}<, {...}>],

"entry":"variable-name",

"include":integer | [{effect-1} <, {effect-2}, ...>],

"informative":True | False,

"noint":True | False,

"nquantlevs":integer,

"quantiles":[double-1 <, double-2, ...>],

"quantsort":True | False,

"start":integer | [{effect-1} <, {effect-2}, ...>],

"stb":True | False

multimember=[{

"details":True | False,

"name":"string",

"noEffect":True | False,

"stdize":True | False,

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

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

}<, {...}>],

nClassLevelsPrint=integer,

output={

"LCLM":"string",

"STDP":"string",

"UCLM":"string",

"casOut":{

"caslib":"string"

"compress":True | False

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

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

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

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

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

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

"pred":"string",

"resid":"string",

"role":"string"

outputTables={

"groupByVarsRaw":True | False,

"includeAll":True | False,

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

"repeated":True | False,

"replace":True | False

partbyfrac={

"seed":integer,

"test":double,

"validate":double

partbyvar={

"name":"variable-name",

"test":"string",

"train":"string",

"validate":"string"

polynomial=[{

"degree":integer,

"details":True | False,

"labelStyle":{

"expand":True | False

"exponent":"string"

"includeName":True | False

"productSymbol":"NONE" | "string"

"mDegree":integer,

"name":"string",

"noSeparate":True | False,

"standardize":{

"method":"MOMENTS" | "MRANGE" | "WMOMENTS"

"options":"CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

"prefix":"NONE" | "string"

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

}<, {...}>],

selection={

"L2":double,

"L2HIGH":double,

"L2LOW":double,

"candidates":integer | "ALL",

"competitive":True | False,

"details":"ALL" | "NONE" | "STEPS" | "SUMMARY",

"enscale":True | False,

"ensteps":integer,

"gamma":double,

"hierarchy":"DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

"maxEffects":integer,

"maxSteps":integer,

"minEffects":integer,

"plots":True | False,

"select":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",

"slEntry":double,

"slStay":double,

"stopHorizon":integer

spline=[{

"basis":"BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

"dataBoundary":True | False,

"degree":integer,

"details":True | False,

"knotMax":double,

"knotMethod":{

"equal":integer

"list":[double-1 <, double-2, ...>]

"listWithBoundary":[double-1 <, double-2, ...>]

"multiscale":{

"endScale":integer

"startScale":integer

}

"rangeFractions":[double-1 <, double-2, ...>]

"knotMin":double,

"name":"string",

"naturalCubic":True | False,

"separate":True | False,

"split":True | False,

"vars":["variable-name-1" <, "variable-name-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",

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

"name":"table-name",

"onDemand":True | False,

"orderBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

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

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

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

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

byLimit=64-bit-integer

specifies that the analysis not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

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

names the classification variables to be used as explanatory variables in the analysis.

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

Alias	classVars, nominal

classLevelsPrint=True | False

when set to False, suppresses the display of class levels.

Default	True

classglobalopts={classopts}

lists options that apply to all classification variables.

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

code={aircodegen}

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

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

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

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

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

"details":True | False

when set to True, requests a table that shows additional details that are related to this effect.

Default	False

* "name":"string"

specifies the name of the effect.

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

cov={qrscov}

specifies the method and parameters for covariance estimation.

"sparsity":{qrssparsity}

specifies the parameters for sparsity estimation.

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

"BF":True | False

when set to True, specifies the Bofinger method of sparsity estimation.

Default	False

"HS":True | False

when set to True, specifies the Hall-Sheather method of sparsity estimation.

Default	False

display={displayTables}

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

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

hidestopstep=True | False

when set to True, hides the stopping step in the selection summary table. This parameter also forces the model selection process to ignore the stopping step in choosing the final model.

Alias	hidestopsteps
Default	False

maxParameters=integer

specifies that models not be fit if the number of parameters exceeds the specified value.

Minimum value	0

model={qrsmodel}

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

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

"clb":True | False

when set to True, displays upper and lower confidence limits for the parameter estimates.

Default	False

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

specifies a list of variables to use as response variables in the model.

Alias	depVar, 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" | "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.

"levelType":"BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

"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" | "string"

"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.

"entry":"variable-name"

specifies the entry variable.

"include":integer | [{effect-1} <, {effect-2}, ...>]

The effect value is specified as follows:

"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.

"informative":True | False

Default	False

"noint":True | False

when set to True, does not include the intercept term in the model.

Default	False

"nquantlevs":integer

specifies the number of quantile levels to be equally spaced in (0,1).

Alias	nquantlev, nqlevs, ntaus, nqlev, ntau, nq
Minimum value	1

"quantiles":[double-1 <, double-2, ...>]

specifies the quantile levels.

Alias	quantile, qlevs, qlev, q, quantlev

"quantsort":True | False

when set to True, sorts all the specified quantile levels in ascending order. This parameter also removes redundant and invalid quantile levels.

Alias	qsort, qtsort, qst
Default	False

"start":integer | [{effect-1} <, {effect-2}, ...>]

The effect value is specified as follows:

"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.

"stb":True | False

Default	False

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

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

nClassLevelsPrint=integer

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

Minimum value	0

output={qrsoutputStatement}

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

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

"LCLM":"string"

names the lower bounds of confidence intervals for the predicted quantiles.

"STDP":"string"

names the standard error of the predicted quantiles.

"UCLM":"string"

names the upper bound of a confidence interval for predicted quantiles.

* "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).

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

"pred":"string"

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

"resid":"string"

names the residual, calculated as ACTUAL minus PREDICTED.

"role":"string"

identifies the training, validation, and test roles for the observations.

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

partbyfrac={partByFracStatement}

specifies the fractions of the data to be used for validation and testing.

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

"seed":integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

"test":double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

"validate":double

Alias	valid
Range	0–1

partbyvar={partByVarStatement}

names the variable and its values used to partition the data into training, validation, and testing roles.

Long form	partbyvar={"name":"variable-name"}
Shortcut form	partbyvar="variable-name"

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

* "name":"variable-name"

names the variable in the input table whose values are used to assign rows to each observation.

"test":"string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

"train":"string"

"validate":"string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

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

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

Alias	poly

selection={selectionStatement}

specifies the method and options for performing model selection.

Long form	selection={"method":"BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"

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

"L2":double

specifies the L2 in elastic net method

Default	0

"L2HIGH":double

specifies the highest value used in the search of L2 in elastic net method

Default	1

"L2LOW":double

specifies the lowest value used in the search of L2 in elastic net method

Default	0

"candidates":integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

"choose":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

"competitive":True | False

Default	False

"details":"ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

Default	SUMMARY

"enscale":True | False

when set to True, applies scale to beta in elastic net method

Default	False

"ensteps":integer

specifies the number of iteration in elastic net method

Default	50

"gamma":double

specifies the exponent of the power transformation that is applied to the parameters in forming the adaptive weights.

Default	1

"hierarchy":"DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

Default	DEFAULT

"maxEffects":integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

"maxSteps":integer

specifies the maximum number of selection steps to perform.

"method":"BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE"

specifies the model selection method.

Default	STEPWISE

"minEffects":integer

specifies the minimum number of effects in any model to consider during backward selection.

"plots":True | False

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

Default	False

"select":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

"slEntry":double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

"slStay":double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

"stop":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

"stopHorizon":integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

Default	3

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

expands variables into spline bases whose form depends on the specified parameters.

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

* 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.

quantreg Action

Fits quantile regression models.

R Syntax

results <– cas.quantreg.quantreg(s,

alpha=double,

attributes=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

byLimit=64-bit-integer,

class=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" | "string",

split=TRUE | FALSE,

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

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

classLevelsPrint=TRUE | FALSE,

classglobalopts=list(

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

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

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

split=TRUE | FALSE

code=list(

casOut=list(

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

comment=TRUE | FALSE,

fmtWdth=integer,

iProb=TRUE | FALSE,

indentSize=integer,

intoCutPt=double,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

pCatAll=TRUE | FALSE,

tabForm=TRUE | FALSE

collection=list( list(

details=TRUE | FALSE,

name="string",

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

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

cov=list(

sparsity=list(

BF=TRUE | FALSE

HS=TRUE | FALSE

)

display=list(

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

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

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

hidestopstep=TRUE | FALSE,

maxParameters=integer,

model=list(

clb=TRUE | FALSE,

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(...)>),

entry="variable-name",

include=integer | list( list(effect-1) <, list(effect-2), ...>),

informative=TRUE | FALSE,

noint=TRUE | FALSE,

nquantlevs=integer,

quantiles=list(double-1 <, double-2, ...>),

quantsort=TRUE | FALSE,

start=integer | list( list(effect-1) <, list(effect-2), ...>),

stb=TRUE | FALSE

multimember=list( list(

details=TRUE | FALSE,

name="string",

noEffect=TRUE | FALSE,

stdize=TRUE | FALSE,

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

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

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

nClassLevelsPrint=integer,

output=list(

LCLM="string",

STDP="string",

UCLM="string",

casOut=list(

caslib="string"

compress=TRUE | FALSE

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

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

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

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

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

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

pred="string",

resid="string",

role="string"

outputTables=list(

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

partbyfrac=list(

seed=integer,

test=double,

validate=double

partbyvar=list(

name="variable-name",

test="string",

train="string",

validate="string"

polynomial=list( list(

degree=integer,

details=TRUE | FALSE,

labelStyle=list(

expand=TRUE | FALSE

exponent="string"

includeName=TRUE | FALSE

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=TRUE | FALSE,

standardize=list(

method="MOMENTS" | "MRANGE" | "WMOMENTS"

options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"

prefix="NONE" | "string"

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

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

selection=list(

L2=double,

L2HIGH=double,

L2LOW=double,

candidates=integer | "ALL",

competitive=TRUE | FALSE,

details="ALL" | "NONE" | "STEPS" | "SUMMARY",

enscale=TRUE | FALSE,

ensteps=integer,

gamma=double,

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",

maxEffects=integer,

maxSteps=integer,

minEffects=integer,

plots=TRUE | FALSE,

select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",

slEntry=double,

slStay=double,

stopHorizon=integer

spline=list( list(

basis="BSPLINE" | "TPF_DEFAULT" | "TPF_NOINT" | "TPF_NOINTANDNOPOWERS" | "TPF_NOPOWERS",

dataBoundary=TRUE | FALSE,

degree=integer,

details=TRUE | FALSE,

knotMax=double,

knotMethod=list(

equal=integer

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

listWithBoundary=list(double-1 <, double-2, ...>)

multiscale=list(

endScale=integer

startScale=integer

)

rangeFractions=list(double-1 <, double-2, ...>)

knotMin=double,

name="string",

naturalCubic=TRUE | FALSE,

separate=TRUE | FALSE,

split=TRUE | FALSE,

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

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

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"

)

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
code	casOut	writes SAS DATA step code for computing predicted values of the fitted model
output	required parametercasOut	creates a table on the server that contains observationwise statistics, which are computed after fitting the model.
outputTables	names	lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

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

Default	0.05
Range	(0, 1)

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

byLimit=64-bit-integer

specifies that the analysis not be performed if the number of BY groups exceeds the specified value.

Minimum value	1

class=list( list(classStatement-1) <, list(classStatement-2), ...>)

names the classification variables to be used as explanatory variables in the analysis.

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

Alias	classVars, nominal

classLevelsPrint=TRUE | FALSE

when set to False, suppresses the display of class levels.

Default	TRUE

classglobalopts=list(classopts)

lists options that apply to all classification variables.

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

code=list(aircodegen)

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

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

collection=list( list(collection-1) <, list(collection-2), ...>)

defines a set of variables that are treated as a single effect that has multiple degrees of freedom.

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

details=TRUE | FALSE

when set to True, requests a table that shows additional details that are related to this effect.

Default	FALSE

* name="string"

specifies the name of the effect.

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

cov=list(qrscov)

specifies the method and parameters for covariance estimation.

sparsity=list(qrssparsity)

specifies the parameters for sparsity estimation.

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

BF=TRUE | FALSE

when set to True, specifies the Bofinger method of sparsity estimation.

Default	FALSE

HS=TRUE | FALSE

when set to True, specifies the Hall-Sheather method of sparsity estimation.

Default	FALSE

display=list(displayTables)

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

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

hidestopstep=TRUE | FALSE

when set to True, hides the stopping step in the selection summary table. This parameter also forces the model selection process to ignore the stopping step in choosing the final model.

Alias	hidestopsteps
Default	FALSE

maxParameters=integer

specifies that models not be fit if the number of parameters exceeds the specified value.

Minimum value	0

model=list(qrsmodel)

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

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

clb=TRUE | FALSE

when set to True, displays upper and lower confidence limits for the parameter estimates.

Default	FALSE

depVars=list( list(responsevar-1) <, list(responsevar-2), ...>)

specifies a list of variables to use as response variables in the model.

Alias	depVar, target

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

name="variable-name"

names the response variable.

options=list(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" | "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.

levelType="BINARY" | "INTERVAL" | "NOMINAL" | "ORDINAL"

Default	INTERVAL

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" | "string"

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

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

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

entry="variable-name"

specifies the entry variable.

include=integer | list( list(effect-1) <, list(effect-2), ...>)

The effect value is specified as follows:

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

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

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

informative=TRUE | FALSE

Default	FALSE

noint=TRUE | FALSE

when set to True, does not include the intercept term in the model.

Default	FALSE

nquantlevs=integer

specifies the number of quantile levels to be equally spaced in (0,1).

Alias	nquantlev, nqlevs, ntaus, nqlev, ntau, nq
Minimum value	1

quantiles=list(double-1 <, double-2, ...>)

specifies the quantile levels.

Alias	quantile, qlevs, qlev, q, quantlev

quantsort=TRUE | FALSE

when set to True, sorts all the specified quantile levels in ascending order. This parameter also removes redundant and invalid quantile levels.

Alias	qsort, qtsort, qst
Default	FALSE

start=integer | list( list(effect-1) <, list(effect-2), ...>)

The effect value is specified as follows:

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

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

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

stb=TRUE | FALSE

Default	FALSE

multimember=list( list(multimember-1) <, list(multimember-2), ...>)

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

nClassLevelsPrint=integer

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

Minimum value	0

output=list(qrsoutputStatement)

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

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

LCLM="string"

names the lower bounds of confidence intervals for the predicted quantiles.

STDP="string"

names the standard error of the predicted quantiles.

UCLM="string"

names the upper bound of a confidence interval for predicted quantiles.

* casOut=list(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).

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

pred="string"

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

resid="string"

names the residual, calculated as ACTUAL minus PREDICTED.

role="string"

identifies the training, validation, and test roles for the observations.

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

partbyfrac=list(partByFracStatement)

specifies the fractions of the data to be used for validation and testing.

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

seed=integer

specifies the seed to use in the random number generator that is used for partitioning the data.

Default	0

test=double

randomly assigns the specified proportion of observations in the input table to the testing role. The sum of the fractions that are specified in the test and validate parameters must be less than 1.

Range	0–1

validate=double

Alias	valid
Range	0–1

partbyvar=list(partByVarStatement)

names the variable and its values used to partition the data into training, validation, and testing roles.

Long form	partbyvar=list(name="variable-name")
Shortcut form	partbyvar="variable-name"

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

* name="variable-name"

names the variable in the input table whose values are used to assign rows to each observation.

test="string"

specifies the formatted value of the variable that is used to assign observations to the testing role.

train="string"

validate="string"

specifies the formatted value of the variable that is used to assign observations to the validation role.

Alias	valid

polynomial=list( list(polynomial-1) <, list(polynomial-2), ...>)

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

Alias	poly

selection=list(selectionStatement)

specifies the method and options for performing model selection.

Long form	selection=list(method="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE")
Shortcut form	selection="BACKWARD" \| "ELASTICNET" \| "FORWARD" \| "FORWARDSWAP" \| "NONE" \| "STEPWISE"

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

L2=double

specifies the L2 in elastic net method

Default	0

L2HIGH=double

specifies the highest value used in the search of L2 in elastic net method

Default	1

L2LOW=double

specifies the lowest value used in the search of L2 in elastic net method

Default	0

candidates=integer | "ALL"

specifies the maximum number of candidates to display at each step of the selection process, when the detail level ALL is specified.

choose="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL"

specifies the criterion for choosing the model. The specified criterion is evaluated at each step of the selection process, and the model that yields the best value of the criterion is chosen.

competitive=TRUE | FALSE

Default	FALSE

details="ALL" | "NONE" | "STEPS" | "SUMMARY"

specifies the level of detail to produce about the selection process.

Default	SUMMARY

enscale=TRUE | FALSE

when set to True, applies scale to beta in elastic net method

Default	FALSE

ensteps=integer

specifies the number of iteration in elastic net method

Default	50

gamma=double

specifies the exponent of the power transformation that is applied to the parameters in forming the adaptive weights.

Default	1

hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS"

Default	DEFAULT

maxEffects=integer

specifies the maximum number of effects in any model to consider during the selection process. This parameter is ignored for backward selection.

maxSteps=integer

specifies the maximum number of selection steps to perform.

method="BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE"

specifies the model selection method.

Default	STEPWISE

minEffects=integer

specifies the minimum number of effects in any model to consider during backward selection.

plots=TRUE | FALSE

when set to True, produces coefficientProgression and selectionSummaryForPlots tables that you can use to produce selection diagnostic plots.

Default	FALSE

select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL"

specifies the criterion to use in determining the order in which effects enter or leave at each step of the selection method. This parameter does not apply to LAR or LASSO selection.

slEntry=double

specifies the significance level for entry when the significance level is used as the select or stop criterion.

Alias	sle
Default	0.05
Range	(0, 1)

slStay=double

specifies the significance level for removal when the significance level is used as the select or stop criterion.

Alias	sls
Default	0.05
Range	(0, 1)

stop="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL"

specifies a criterion that to use for stopping the selection process. If you do not specify a stop criterion, then the criterion that is used to select the model is also used as the stop criterion.

stopHorizon=integer

specifies the number of consecutive steps at which the stop criterion must worsen in order for a local extremum to be detected.

Default	3

spline=list( list(spline-1) <, list(spline-2), ...>)

expands variables into spline bases whose form depends on the specified parameters.

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

* 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).

weight="variable-name"

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

Last updated: November 02, 2020