Quantile Regression Modeling Action Set

Provides actions for performing quantile regression

quantreg Action

Fits quantile regression models.

CASL Syntax

quantreg.quantreg <result=results> <status=rc> /
algorithm={
svo={
maxIt=integer
tol=double
}
},
alpha=double,
attributes={{
format="string",
formattedLength=integer,
label="string",
required parameter 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",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
classglobalopts={
countMissing=TRUE | FALSE,
descending=TRUE | FALSE,
ignoreMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE
},
classLevelsPrint=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
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where={"string-1" <, "string-2", ...>}
},
comment=TRUE | FALSE,
fmtWdth=integer,
indentSize=integer,
intoCutPt=double,
iProb=TRUE | FALSE,
labelId=integer,
lineSize=integer,
noTrim=TRUE | FALSE,
pCatAll=TRUE | FALSE,
tabForm=TRUE | FALSE
},
collection={{
details=TRUE | FALSE,
required parameter name="string",
required parameter 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"
}, {...}},
effects={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter 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,
required parameter name="string",
noEffect=TRUE | FALSE,
stdize=TRUE | FALSE,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>},
weight={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
nClassLevelsPrint=integer,
output={
ALLSTATS=TRUE | FALSE,
required parameter 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"
promote=TRUE | FALSE
replace=TRUE | FALSE
replication=integer
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where={"string-1" <, "string-2", ...>}
},
copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>},
LCLM="string",
pred="string",
resid="string",
role="string",
STDP="string",
UCLM="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={
required parameter 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,
required parameter name="string",
noSeparate=TRUE | FALSE,
standardize={
method="MOMENTS" | "MRANGE" | "WMOMENTS"
options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
prefix="NONE" | "string"
},
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
selection={
candidates=integer | "ALL",
choose="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL",
competitive=TRUE | FALSE,
details="ALL" | "NONE" | "STEPS" | "SUMMARY",
elasticNetOptions={
absFConv=double
fConv=double
gConv=double
lambda={double-1 <, double-2, ...>}
mixing={double-1 <, double-2, ...>}
numLambda=integer
rho=double
solver="ADMM" | "BFGS" | "LBFGS" | "NLP"
},
enscale=TRUE | FALSE,
ensteps=integer,
gamma=double,
hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",
kappa={double-1 <, double-2, ...>},
L2=double,
L2HIGH=double,
L2LOW=double,
maxEffects=integer,
maxSteps=integer,
method="BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE",
minEffects=integer,
plots=TRUE | FALSE,
relaxed=TRUE | FALSE,
select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",
slEntry=double,
slStay=double,
stop="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL",
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,
required parameter name="string",
naturalCubic=TRUE | FALSE,
separate=TRUE | FALSE,
split=TRUE | FALSE,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
store={
caslib="string",
label="string",
lifetime=64-bit-integer,
memoryFormat="DVR" | "INHERIT" | "STANDARD",
name="table-name",
promote=TRUE | FALSE,
replace=TRUE | FALSE,
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
},
required parameter table={
caslib="string",
computedOnDemand=TRUE | FALSE,
computedVars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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",
required parameter 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},
required parameter name="table-name",
orderBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
singlePass=TRUE | FALSE,
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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 | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
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}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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.

 store

—

stores regression models to a binary large object (BLOB).

Parameter Descriptions

algorithm={qrsalgorithm}

Algorithm for model fitting

Alias solver
svo={qrsalgsvo}

Support vecter optimization

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

maxIt=integer

Maximum iteration number

Minimum value 1
tol=double

Tolerance for prime-dual gap

Default 1E-06
Minimum value 0

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).

Aliases classVars
nominal

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).

classLevelsPrint=TRUE | FALSE

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

Default TRUE

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

Aliases depVar
target
name="variable-name"

names the response variable.

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 for the specified selection method. 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", ...>}

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.

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).

Aliases nquantlev
nqlevs
ntaus
nqlev
ntau
nq
Minimum value 1
quantiles={double-1 <, double-2, ...>}

specifies the quantile levels.

Aliases 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.

Aliases 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", ...>}

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.

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:

ALLSTATS=TRUE | FALSE

when set to True, requests all available statistics.

Default FALSE
* casOut={casouttable}

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) 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.

LCLM="string"

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

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.

STDP="string"

names the standard error of the predicted quantiles.

UCLM="string"

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

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 roles 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:

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
elasticNetOptions={enOptions}

specifies options to use in performing elastic net selection methods.

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

absFConv=double

specifies the absolute function difference convergence criterion.

Alias abstol
Default 1E-08
Minimum value 0
fConv=double

specifies the relative function difference convergence criterion.

Alias fTol
Minimum value 0
gConv=double

specifies the relative gradient convergence criterion.

Alias gTol
Minimum value 0
lambda={double-1 <, double-2, ...>}

specifies the regularization parameters in the elastic net selection method.

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

specifies the elastic net mixing parameter.

numLambda=integer

specifies the number of regularization parameters in the elastic net selection method.

Alias nLambda
Default 0
Minimum value 0
rho=double

specifies the scaling factor to use in computing minimum regularization parameter.

Range (0, 1)
solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

enscale=TRUE | FALSE

when set to True, applies scaling to beta in the elastic net selection method.

Default FALSE
ensteps=integer

specifies the number of iterations to use in the elastic net selection 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
kappa={double-1 <, double-2, ...>}

specifies the coefficients in the relaxed LASSO method.

L2=double

specifies the L2 parameter in the elastic net selection method.

Default 0
L2HIGH=double

specifies the upper bound to use in searching for the L2 parameter in the elastic net selection method.

Alias maxL2
Default 1
L2LOW=double

specifies the lower bound to use in searching for the L2 parameter in the elastic net selection method.

Alias minL2
Default 0
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
relaxed=TRUE | FALSE

when set to True, applies the relaxed LASSO method.

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).

store={casouttable}

stores regression models to a binary large object (BLOB).

Alias savestate
Long form store={name="table-name"}
Shortcut form store="table-name"

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

caslib="string"

specifies the name of the caslib for the output table.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default 0
Minimum value 0
memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default INHERIT
DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

use the default memory format that is set for the server. By default, the server uses the standard memory format. If an administrator sets the CAS_DEFAULT_MEMORY_FORMAT environment variable to DVR, then the DVR memory format is set as the default for the server.

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=TRUE | FALSE

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default FALSE
replace=TRUE | FALSE

when set to True, overwrites an existing table that has the same name.

Default FALSE
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) 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{
algorithm={
svo={
maxIt=integer
tol=double
}
},
alpha=double,
attributes={{
format="string",
formattedLength=integer,
label="string",
required parameter 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",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=true | false,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
classglobalopts={
countMissing=true | false,
descending=true | false,
ignoreMissing=true | false,
levelizeRaw=true | false,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=true | false
},
classLevelsPrint=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
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where={"string-1" <, "string-2", ...>}
},
comment=true | false,
fmtWdth=integer,
indentSize=integer,
intoCutPt=double,
iProb=true | false,
labelId=integer,
lineSize=integer,
noTrim=true | false,
pCatAll=true | false,
tabForm=true | false
},
collection={{
details=true | false,
required parameter name="string",
required parameter 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"
}, {...}},
effects={{
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest={"string-1" <, "string-2", ...>},
required parameter 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,
required parameter name="string",
noEffect=true | false,
stdize=true | false,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>},
weight={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
nClassLevelsPrint=integer,
output={
ALLSTATS=true | false,
required parameter 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"
promote=true | false
replace=true | false
replication=integer
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where={"string-1" <, "string-2", ...>}
},
copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | {"variable-name-1" <, "variable-name-2", ...>},
LCLM="string",
pred="string",
resid="string",
role="string",
STDP="string",
UCLM="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={
required parameter 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,
required parameter name="string",
noSeparate=true | false,
standardize={
method="MOMENTS" | "MRANGE" | "WMOMENTS"
options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
prefix="NONE" | "string"
},
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
selection={
candidates=integer | "ALL",
choose="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL",
competitive=true | false,
details="ALL" | "NONE" | "STEPS" | "SUMMARY",
elasticNetOptions={
absFConv=double
fConv=double
gConv=double
lambda={double-1 <, double-2, ...>}
mixing={double-1 <, double-2, ...>}
numLambda=integer
rho=double
solver="ADMM" | "BFGS" | "LBFGS" | "NLP"
},
enscale=true | false,
ensteps=integer,
gamma=double,
hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",
kappa={double-1 <, double-2, ...>},
L2=double,
L2HIGH=double,
L2LOW=double,
maxEffects=integer,
maxSteps=integer,
method="BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE",
minEffects=integer,
plots=true | false,
relaxed=true | false,
select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",
slEntry=double,
slStay=double,
stop="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL",
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,
required parameter name="string",
naturalCubic=true | false,
separate=true | false,
split=true | false,
required parameter vars={"variable-name-1" <, "variable-name-2", ...>}
}, {...}},
store={
caslib="string",
label="string",
lifetime=64-bit-integer,
memoryFormat="DVR" | "INHERIT" | "STANDARD",
name="table-name",
promote=true | false,
replace=true | false,
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
},
required parameter table={
caslib="string",
computedOnDemand=true | false,
computedVars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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",
required parameter 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},
required parameter name="table-name",
orderBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
singlePass=true | false,
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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 | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
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}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter 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.

 store

—

stores regression models to a binary large object (BLOB).

Parameter Descriptions

algorithm={qrsalgorithm}

Algorithm for model fitting

Alias solver
svo={qrsalgsvo}

Support vecter optimization

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

maxIt=integer

Maximum iteration number

Minimum value 1
tol=double

Tolerance for prime-dual gap

Default 1E-06
Minimum value 0

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).

Aliases classVars
nominal

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).

classLevelsPrint=true | false

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

Default true

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

Aliases depVar
target
name="variable-name"

names the response variable.

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 for the specified selection method. 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", ...>}

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.

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).

Aliases nquantlev
nqlevs
ntaus
nqlev
ntau
nq
Minimum value 1
quantiles={double-1 <, double-2, ...>}

specifies the quantile levels.

Aliases 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.

Aliases 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", ...>}

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.

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:

ALLSTATS=true | false

when set to True, requests all available statistics.

Default false
* casOut={casouttable}

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) 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.

LCLM="string"

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

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.

STDP="string"

names the standard error of the predicted quantiles.

UCLM="string"

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

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 roles 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:

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
elasticNetOptions={enOptions}

specifies options to use in performing elastic net selection methods.

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

absFConv=double

specifies the absolute function difference convergence criterion.

Alias abstol
Default 1E-08
Minimum value 0
fConv=double

specifies the relative function difference convergence criterion.

Alias fTol
Minimum value 0
gConv=double

specifies the relative gradient convergence criterion.

Alias gTol
Minimum value 0
lambda={double-1 <, double-2, ...>}

specifies the regularization parameters in the elastic net selection method.

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

specifies the elastic net mixing parameter.

numLambda=integer

specifies the number of regularization parameters in the elastic net selection method.

Alias nLambda
Default 0
Minimum value 0
rho=double

specifies the scaling factor to use in computing minimum regularization parameter.

Range (0, 1)
solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

enscale=true | false

when set to True, applies scaling to beta in the elastic net selection method.

Default false
ensteps=integer

specifies the number of iterations to use in the elastic net selection 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
kappa={double-1 <, double-2, ...>}

specifies the coefficients in the relaxed LASSO method.

L2=double

specifies the L2 parameter in the elastic net selection method.

Default 0
L2HIGH=double

specifies the upper bound to use in searching for the L2 parameter in the elastic net selection method.

Alias maxL2
Default 1
L2LOW=double

specifies the lower bound to use in searching for the L2 parameter in the elastic net selection method.

Alias minL2
Default 0
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
relaxed=true | false

when set to True, applies the relaxed LASSO method.

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).

store={casouttable}

stores regression models to a binary large object (BLOB).

Alias savestate
Long form store={name="table-name"}
Shortcut form store="table-name"

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

caslib="string"

specifies the name of the caslib for the output table.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default 0
Minimum value 0
memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default INHERIT
DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

use the default memory format that is set for the server. By default, the server uses the standard memory format. If an administrator sets the CAS_DEFAULT_MEMORY_FORMAT environment variable to DVR, then the DVR memory format is set as the default for the server.

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=true | false

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default false
replace=true | false

when set to True, overwrites an existing table that has the same name.

Default false
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) 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(
algorithm={
"svo":{
"maxIt":integer
"tol":double
}
},
alpha=double,
attributes=[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "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",
"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
"ref":"FIRST" | "LAST" | double | "string",
"split":True | False,
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
classglobalopts={
"countMissing":True | False,
"descending":True | False,
"ignoreMissing":True | False,
"levelizeRaw":True | False,
"maxLev":integer,
"order":"FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
"param":"BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
"ref":"FIRST" | "LAST" | double | "string",
"split":True | False
},
classLevelsPrint=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
"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"
"threadBlockSize":64-bit-integer
"timeStamp":"string"
"where":["string-1" <, "string-2", ...>]
},
"comment":True | False,
"fmtWdth":integer,
"indentSize":integer,
"intoCutPt":double,
"iProb":True | False,
"labelId":integer,
"lineSize":integer,
"noTrim":True | False,
"pCatAll":True | False,
"tabForm":True | False
},
collection=[{
"details":True | False,
required parameter "name":"string",
required parameter "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"
}<, {...}>],
"effects":[{
"interaction":"BAR" | "CROSS" | "NONE",
"maxInteract":integer,
"nest":["string-1" <, "string-2", ...>],
required parameter "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,
required parameter "name":"string",
"noEffect":True | False,
"stdize":True | False,
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>],
"weight":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
nClassLevelsPrint=integer,
output={
"ALLSTATS":True | False,
required parameter "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"
"promote":True | False
"replace":True | False
"replication":integer
"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"
"threadBlockSize":64-bit-integer
"timeStamp":"string"
"where":["string-1" <, "string-2", ...>]
},
"copyVars":"ALL" | "ALL_MODEL" | "ALL_NUMERIC" | ["variable-name-1" <, "variable-name-2", ...>],
"LCLM":"string",
"pred":"string",
"resid":"string",
"role":"string",
"STDP":"string",
"UCLM":"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={
required parameter "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,
required parameter "name":"string",
"noSeparate":True | False,
"standardize":{
"method":"MOMENTS" | "MRANGE" | "WMOMENTS"
"options":"CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
"prefix":"NONE" | "string"
},
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
selection={
"candidates":integer | "ALL",
"choose":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL",
"competitive":True | False,
"details":"ALL" | "NONE" | "STEPS" | "SUMMARY",
"elasticNetOptions":{
"absFConv":double
"fConv":double
"gConv":double
"lambda_":[double-1 <, double-2, ...>]
"mixing":[double-1 <, double-2, ...>]
"numLambda":integer
"rho":double
"solver":"ADMM" | "BFGS" | "LBFGS" | "NLP"
},
"enscale":True | False,
"ensteps":integer,
"gamma":double,
"hierarchy":"DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",
"kappa":[double-1 <, double-2, ...>],
"L2":double,
"L2HIGH":double,
"L2LOW":double,
"maxEffects":integer,
"maxSteps":integer,
"method":"BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE",
"minEffects":integer,
"plots":True | False,
"relaxed":True | False,
"select":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",
"slEntry":double,
"slStay":double,
"stop":"ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL",
"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,
required parameter "name":"string",
"naturalCubic":True | False,
"separate":True | False,
"split":True | False,
required parameter "vars":["variable-name-1" <, "variable-name-2", ...>]
}<, {...}>],
store={
"caslib":"string",
"label":"string",
"lifetime":64-bit-integer,
"memoryFormat":"DVR" | "INHERIT" | "STANDARD",
"name":"table-name",
"promote":True | False,
"replace":True | False,
"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"
},
required parameter table={
"caslib":"string",
"computedOnDemand":True | False,
"computedVars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "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",
required parameter "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},
required parameter "name":"table-name",
"orderBy":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
"singlePass":True | False,
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "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 | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
"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}
required parameter "name":"table-name"
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "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.

 store

—

stores regression models to a binary large object (BLOB).

Parameter Descriptions

algorithm={qrsalgorithm}

Algorithm for model fitting

Alias solver
"svo":{qrsalgsvo}

Support vecter optimization

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

"maxIt":integer

Maximum iteration number

Minimum value 1
"tol":double

Tolerance for prime-dual gap

Default 1E-06
Minimum value 0

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).

Aliases classVars
nominal

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).

classLevelsPrint=True | False

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

Default True

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

Aliases depVar
target
"name":"variable-name"

names the response variable.

"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 for the specified selection method. 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", ...>]

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.

"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).

Aliases nquantlev
nqlevs
ntaus
nqlev
ntau
nq
Minimum value 1
"quantiles":[double-1 <, double-2, ...>]

specifies the quantile levels.

Aliases 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.

Aliases 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", ...>]

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.

"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:

"ALLSTATS":True | False

when set to True, requests all available statistics.

Default False
* "casOut":{casouttable}

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) 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.

"LCLM":"string"

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

"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.

"STDP":"string"

names the standard error of the predicted quantiles.

"UCLM":"string"

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

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 roles 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:

"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
"elasticNetOptions":{enOptions}

specifies options to use in performing elastic net selection methods.

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

"absFConv":double

specifies the absolute function difference convergence criterion.

Alias abstol
Default 1E-08
Minimum value 0
"fConv":double

specifies the relative function difference convergence criterion.

Alias fTol
Minimum value 0
"gConv":double

specifies the relative gradient convergence criterion.

Alias gTol
Minimum value 0
"lambda_":[double-1 <, double-2, ...>]

specifies the regularization parameters in the elastic net selection method.

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

specifies the elastic net mixing parameter.

"numLambda":integer

specifies the number of regularization parameters in the elastic net selection method.

Alias nLambda
Default 0
Minimum value 0
"rho":double

specifies the scaling factor to use in computing minimum regularization parameter.

Range (0, 1)
"solver":"ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

"enscale":True | False

when set to True, applies scaling to beta in the elastic net selection method.

Default False
"ensteps":integer

specifies the number of iterations to use in the elastic net selection 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
"kappa":[double-1 <, double-2, ...>]

specifies the coefficients in the relaxed LASSO method.

"L2":double

specifies the L2 parameter in the elastic net selection method.

Default 0
"L2HIGH":double

specifies the upper bound to use in searching for the L2 parameter in the elastic net selection method.

Alias maxL2
Default 1
"L2LOW":double

specifies the lower bound to use in searching for the L2 parameter in the elastic net selection method.

Alias minL2
Default 0
"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
"relaxed":True | False

when set to True, applies the relaxed LASSO method.

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).

store={casouttable}

stores regression models to a binary large object (BLOB).

Alias savestate
Long form store={"name":"table-name"}
Shortcut form store="table-name"

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

"caslib":"string"

specifies the name of the caslib for the output table.

"label":"string"

specifies the descriptive label to associate with the table.

"lifetime":64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default 0
Minimum value 0
"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default INHERIT
DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

use the default memory format that is set for the server. By default, the server uses the standard memory format. If an administrator sets the CAS_DEFAULT_MEMORY_FORMAT environment variable to DVR, then the DVR memory format is set as the default for the server.

STANDARD

use the standard memory format.

"name":"table-name"

specifies the name for the output table.

"promote":True | False

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default False
"replace":True | False

when set to True, overwrites an existing table that has the same name.

Default False
"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) 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,
algorithm=list(
svo=list(
maxIt=integer
tol=double
)
),
alpha=double,
attributes=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter 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",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE,
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
classglobalopts=list(
countMissing=TRUE | FALSE,
descending=TRUE | FALSE,
ignoreMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
order="FORMATTED" | "FREQ" | "FREQFORMATTED" | "FREQINTERNAL" | "INTERNAL",
param="BTH" | "EFFECT" | "GLM" | "ORDINAL" | "ORTHBTH" | "ORTHEFFECT" | "ORTHORDINAL" | "ORTHPOLY" | "ORTHREF" | "POLYNOMIAL" | "REFERENCE",
ref="FIRST" | "LAST" | double | "string",
split=TRUE | FALSE
),
classLevelsPrint=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
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
threadBlockSize=64-bit-integer
timeStamp="string"
where=list("string-1" <, "string-2", ...>)
),
comment=TRUE | FALSE,
fmtWdth=integer,
indentSize=integer,
intoCutPt=double,
iProb=TRUE | FALSE,
labelId=integer,
lineSize=integer,
noTrim=TRUE | FALSE,
pCatAll=TRUE | FALSE,
tabForm=TRUE | FALSE
),
collection=list( list(
details=TRUE | FALSE,
required parameter name="string",
required parameter 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"
) <, list(...)>),
effects=list( list(
interaction="BAR" | "CROSS" | "NONE",
maxInteract=integer,
nest=list("string-1" <, "string-2", ...>),
required parameter 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,
required parameter name="string",
noEffect=TRUE | FALSE,
stdize=TRUE | FALSE,
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>),
weight=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
nClassLevelsPrint=integer,
output=list(
ALLSTATS=TRUE | FALSE,
required parameter 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"
promote=TRUE | FALSE
replace=TRUE | FALSE
replication=integer
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
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", ...>),
LCLM="string",
pred="string",
resid="string",
role="string",
STDP="string",
UCLM="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(
required parameter 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,
required parameter name="string",
noSeparate=TRUE | FALSE,
standardize=list(
method="MOMENTS" | "MRANGE" | "WMOMENTS"
options="CENTER" | "CENTERSCALE" | "NONE" | "SCALE"
prefix="NONE" | "string"
),
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
selection=list(
candidates=integer | "ALL",
choose="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "VALIDATE" | "VALIDATE_ACL",
competitive=TRUE | FALSE,
details="ALL" | "NONE" | "STEPS" | "SUMMARY",
elasticNetOptions=list(
absFConv=double
fConv=double
gConv=double
lambda=list(double-1 <, double-2, ...>)
mixing=list(double-1 <, double-2, ...>)
numLambda=integer
rho=double
solver="ADMM" | "BFGS" | "LBFGS" | "NLP"
),
enscale=TRUE | FALSE,
ensteps=integer,
gamma=double,
hierarchy="DEFAULT" | "NONE" | "SINGLE" | "SINGLECLASS",
kappa=list(double-1 <, double-2, ...>),
L2=double,
L2HIGH=double,
L2LOW=double,
maxEffects=integer,
maxSteps=integer,
method="BACKWARD" | "ELASTICNET" | "FORWARD" | "FORWARDSWAP" | "NONE" | "STEPWISE",
minEffects=integer,
plots=TRUE | FALSE,
relaxed=TRUE | FALSE,
select="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "R1" | "SBC" | "SL" | "VALIDATE_ACL",
slEntry=double,
slStay=double,
stop="ADJR1" | "AIC" | "AICC" | "DEFAULT" | "NONE" | "R1" | "SBC" | "SL" | "VALIDATE" | "VALIDATE_ACL",
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,
required parameter name="string",
naturalCubic=TRUE | FALSE,
separate=TRUE | FALSE,
split=TRUE | FALSE,
required parameter vars=list("variable-name-1" <, "variable-name-2", ...>)
) <, list(...)>),
store=list(
caslib="string",
label="string",
lifetime=64-bit-integer,
memoryFormat="DVR" | "INHERIT" | "STANDARD",
name="table-name",
promote=TRUE | FALSE,
replace=TRUE | FALSE,
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"
),
required parameter table=list(
caslib="string",
computedOnDemand=TRUE | FALSE,
computedVars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter 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",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
groupByMode="NOSORT" | "REDISTRIBUTE",
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters),
required parameter name="table-name",
orderBy=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
singlePass=TRUE | FALSE,
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
where="where-expression",
whereTable=list(
casLib="string"
dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)
required parameter name="table-name"
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter 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.

 store

—

stores regression models to a binary large object (BLOB).

Parameter Descriptions

algorithm=list(qrsalgorithm)

Algorithm for model fitting

Alias solver
svo=list(qrsalgsvo)

Support vecter optimization

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

maxIt=integer

Maximum iteration number

Minimum value 1
tol=double

Tolerance for prime-dual gap

Default 1E-06
Minimum value 0

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).

Aliases classVars
nominal

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).

classLevelsPrint=TRUE | FALSE

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

Default TRUE

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", ...>)

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

Aliases depVar
target
name="variable-name"

names the response variable.

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

specifies effects to include at the start of the selection process for the specified selection method. 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=list("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=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

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).

Aliases nquantlev
nqlevs
ntaus
nqlev
ntau
nq
Minimum value 1
quantiles=list(double-1 <, double-2, ...>)

specifies the quantile levels.

Aliases 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.

Aliases qsort
qtsort
qst
Default FALSE
start=integer | list( list(effect-1) <, list(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=list("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=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

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=list( list(multimember-1) <, list(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=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:

ALLSTATS=TRUE | FALSE

when set to True, requests all available statistics.

Default FALSE
* casOut=list(casouttable)

specifies the settings for an output table.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

copyVars="ALL" | "ALL_MODEL" | "ALL_NUMERIC" | list("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.

LCLM="string"

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

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.

STDP="string"

names the standard error of the predicted quantiles.

UCLM="string"

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

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

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=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 roles 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=list( list(polynomial-1) <, list(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=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:

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
elasticNetOptions=list(enOptions)

specifies options to use in performing elastic net selection methods.

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

absFConv=double

specifies the absolute function difference convergence criterion.

Alias abstol
Default 1E-08
Minimum value 0
fConv=double

specifies the relative function difference convergence criterion.

Alias fTol
Minimum value 0
gConv=double

specifies the relative gradient convergence criterion.

Alias gTol
Minimum value 0
lambda=list(double-1 <, double-2, ...>)

specifies the regularization parameters in the elastic net selection method.

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

specifies the elastic net mixing parameter.

numLambda=integer

specifies the number of regularization parameters in the elastic net selection method.

Alias nLambda
Default 0
Minimum value 0
rho=double

specifies the scaling factor to use in computing minimum regularization parameter.

Range (0, 1)
solver="ADMM" | "BFGS" | "LBFGS" | "NLP"

specifies a solver for elastic net selection.

enscale=TRUE | FALSE

when set to True, applies scaling to beta in the elastic net selection method.

Default FALSE
ensteps=integer

specifies the number of iterations to use in the elastic net selection 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
kappa=list(double-1 <, double-2, ...>)

specifies the coefficients in the relaxed LASSO method.

L2=double

specifies the L2 parameter in the elastic net selection method.

Default 0
L2HIGH=double

specifies the upper bound to use in searching for the L2 parameter in the elastic net selection method.

Alias maxL2
Default 1
L2LOW=double

specifies the lower bound to use in searching for the L2 parameter in the elastic net selection method.

Alias minL2
Default 0
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
relaxed=TRUE | FALSE

when set to True, applies the relaxed LASSO method.

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).

store=list(casouttable)

stores regression models to a binary large object (BLOB).

Alias savestate
Long form store=list(name="table-name")
Shortcut form store="table-name"

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

caslib="string"

specifies the name of the caslib for the output table.

label="string"

specifies the descriptive label to associate with the table.

lifetime=64-bit-integer

specifies the number of seconds to keep the table in memory after it is last accessed. The table is dropped if it is not accessed for the specified number of seconds.

Default 0
Minimum value 0
memoryFormat="DVR" | "INHERIT" | "STANDARD"

specifies the memory format for the output table.

Default INHERIT
DVR

use the duplicate value reduction memory format. This memory format can reduce the memory consumption and file size when the input data contains duplicate values.

INHERIT

use the default memory format that is set for the server. By default, the server uses the standard memory format. If an administrator sets the CAS_DEFAULT_MEMORY_FORMAT environment variable to DVR, then the DVR memory format is set as the default for the server.

STANDARD

use the standard memory format.

name="table-name"

specifies the name for the output table.

promote=TRUE | FALSE

when set to True, adds the output table with a global scope. This enables other sessions to access the table, subject to access controls. The target caslib must also have a global scope.

Default FALSE
replace=TRUE | FALSE

when set to True, overwrites an existing table that has the same name.

Default FALSE
tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE"

Specifies the Table Redistribution Policy when the number of worker pods increases on a running CAS server.

DEFER

Defer redistribution policy selection to higher-level entity.

NOREDIST

Do not redistribute table data when the number of worker pods changes on a running CAS server.

REBALANCE

Rebalance table data when the number of worker pods changes on a running CAS server.

* table=list(castable)

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 1) parameter (Appendix A: Common Parameters).

weight="variable-name"

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

Last updated: March 05, 2026