Proportional Hazards Regression Modeling Action Set

Provides actions for fitting proportional hazards model

cox Action

Fit Cox regression model.

CASL Syntax
Summary: Input and Output Tables
Parameter Descriptions

CASL Syntax

phreg.cox <result=results> <status=rc> /

alpha=double,

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

class={{

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

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

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

split=TRUE | FALSE,

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",

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

split=TRUE | FALSE

classLevelsPrint=TRUE | FALSE,

clb=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"

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,

cumHaz=TRUE | FALSE,

fmtWdth=integer,

indentSize=integer,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

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

showTime=TRUE | FALSE,

survival=TRUE | FALSE,

tabForm=TRUE | FALSE,

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

collection={{

details=TRUE | FALSE,

name="string",

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

}, {...}},

corrB=TRUE | FALSE,

covB=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

freq="variable-name",

hessian=TRUE | FALSE,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

logLikeNull=TRUE | FALSE,

model={

censor="variable-name",

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

clb=TRUE | FALSE,

depVars={{

name="variable-name"

}, {...}},

effects={{

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

maxInteract=integer,

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

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

}, {...}},

entry="variable-name",

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

informative=TRUE | FALSE,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

offset="variable-name",

ss3=TRUE | FALSE,

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

multimember={{

details=TRUE | FALSE,

name="string",

noEffect=TRUE | FALSE,

stdize=TRUE | FALSE,

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

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

}, {...}},

multipass=TRUE | FALSE,

nClassLevelsPrint=integer,

noStdErr=TRUE | FALSE,

optimization={

absConv=double,

absFConv=double,

absGConv=double,

absXConv=double,

fConv=double,

fConv2=double,

gConv=double,

gConv2=double,

itHist="NONE" | "SUMMARY",

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

xConv=double

output={

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", ...>},

cumHaz="string",

dfBeta="string",

ld="string",

resDev="string",

resMart="string",

resSch="string",

resSco="string",

role="string",

stdXBeta="string",

survival="string",

wtResSch="string",

xBeta="string"

outputTables={

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

partByFrac={

seed=integer,

test=double,

validate=double

partByVar={

name="variable-name",

test="string",

train="string",

validate="string"

polynomial={{

degree=integer,

details=TRUE | FALSE,

labelStyle={

expand=TRUE | FALSE

exponent="string"

includeName=TRUE | FALSE

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=TRUE | FALSE,

standardize={

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

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

prefix="NONE" | "string"

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

}, {...}},

selection={

candidates=integer | "ALL",

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,

fast=TRUE | FALSE,

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

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

L2=double,

L2HIGH=double,

L2LOW=double,

maxEffects=integer,

maxSteps=integer,

method="BACKWARD" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE",

minEffects=integer,

plots=TRUE | FALSE,

relaxed=TRUE | FALSE,

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

slEntry=double,

slStay=double,

stopHorizon=integer

spline={{

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

dataBoundary=TRUE | FALSE,

degree=integer,

details=TRUE | FALSE,

knotMax=double,

knotMethod={

equal=integer

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

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

multiscale={

endScale=integer

startScale=integer

}

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

knotMin=double,

name="string",

naturalCubic=TRUE | FALSE,

separate=TRUE | FALSE,

split=TRUE | FALSE,

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

}, {...}},

ss3=TRUE | FALSE,

strata="variable-name",

strataMissing=TRUE | FALSE,

table={

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

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

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

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

name="table-name",

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

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

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

weight="variable-name"

;

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies input data table

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

Parameter Descriptions

alpha=double

specifies the significance level to use for 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

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
Aliases	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

clb=TRUE | FALSE

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

Default	FALSE

code={coxCodegen}

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

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

casOut={casouttable}

specifies the settings for an output table.

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

caslib="string"

specifies the name of the caslib for the output table.

compress=TRUE | FALSE

when set to True, applies data compression to the table.

Default	FALSE

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

specifies the list of variables to create indexes for in the output data.

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

maxMemSize=64-bit-integer

specifies the maximum amount of memory, in bytes, that each thread should allocate for in-memory blocks before converting to a memory-mapped file. Files are written in the directories that are specified in the CAS_DISK_CACHE environment variable.

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

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

replication=integer

specifies the number of copies of the table to make for fault tolerance. Larger values result in slower performance and use more memory, but provide high availability for data in the event of a node failure. Data redundancy applies to distributed servers only.

Default	1
Minimum value	0

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.

threadBlockSize=64-bit-integer

specifies the number of bytes to use for blocks in the output table. The blocks are read by threads. Gradually increase this value when you have a large table with millions or billions of rows and you are tuning for performance. Larger values can increase performance with indexed tables. However, if the value is too large, then you can cause thread starvation due to too few blocks for threads to work on.

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

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

specifies one or more expressions for subsetting the output data. When multiple expressions are specified, the expressions are effectively combined using AND to form the final output filter. If an expression contains quoted values, use nested quotation marks.

comment=TRUE | FALSE

when set to True, adds comments to the DATA step code.

Default	FALSE

cumHaz=TRUE | FALSE

when set to True, generates code for predicting cumulative hazards.

Default	FALSE

fmtWdth=integer

specifies the width to use for formatting derived numbers such as parameter estimates in the DATA step code.

Alias	fmtWidth
Default	20
Range	0–32

indentSize=integer

specifies the number of spaces to indent the DATA step code for each level.

Default	3
Range	0–10

labelId=integer

specifies the label ID to use in array names and statement labels in the DATA step code. By default, a random positive integer is used.

lineSize=integer

specifies the line size for the generated code.

Default	120
Range	64–254

noTrim=TRUE | FALSE

when set to True, bases the comparison of variables with formatted values on the full format width with padding. By default, leading and trailing blanks are removed from the formatted values.

Default	FALSE

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

specifies a list of quantile probabilities. Survival probabilities or cumulative hazards are predicted at the corresponding quantiles.

showTime=TRUE | FALSE

when set to True, generates code for displaying the time points.

Default	FALSE

survival=TRUE | FALSE

when set to True, generates code for predicting survival probabilities

Default	TRUE

tabForm=TRUE | FALSE

when set to True, generates the code in a way that is appropriate for storing in a table.

Alias	tableForm
Default	FALSE

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

specifies a list of time points at which the survival probabilities or cumulative hazards are predicted.

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.

corrB=TRUE | FALSE

when set to True, displays the correlation matrix of the parameters.

Default	FALSE

covB=TRUE | FALSE

when set to True, displays the covariance matrix of the parameters.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

hessian=TRUE | FALSE

when set to True, uses the analytical Hessian instead of the finite difference Hessian.

Default	FALSE

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

logLikeNull=TRUE | FALSE

when set to True, displays the -2 log likelihood of the NULL model.

Alias	m2ll0
Default	FALSE

model={coxModel}

names the dependent variable, explanatory effects and model options.

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

censor="variable-name"

specifies the censor variable.

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

specifies a list of numeric values that identify censored observations.

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
Aliases	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", ...>}

* 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

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

offset="variable-name"

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

ss3=TRUE | FALSE

when set to True, performs Type 3 effect tests. Under full-rank parameterization or for models with constructed effects, Type 3 effect tests are replaced by joint tests. The joint test for an effect tests whether all the parameters associated with that effect are zero. Such joint tests might not be equivalent to Type 3 effect tests under GLM parameterization.

Default	FALSE

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

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

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).

multipass=TRUE | FALSE

when set to True, levelizes the input data table everytime it is read.

Default	FALSE

nClassLevelsPrint=integer

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

Minimum value	0

noStdErr=TRUE | FALSE

when set to True, does not compute the covariance matrix or any statistic that depends on it.

Default	FALSE

optimization={optimizationStatement}

specifies the technique and options for performing the optimization.

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	absTol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

absXConv=double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

itHist="NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

NEWRAP

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

NMSIMP

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

NONE

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

NRRIDG

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

TRUREG

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

xConv=double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output={coxOutputStatement}

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

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

* 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.

cumHaz="string"

names the cumulative hazard at the given time.

dfBeta="string"

specifies the prefix for the dfBeta statistic names. A dfBeta statistic name is the concatenation of this prefix with an underscore and the name of a parameter.

ld="string"

names the likelihood displacement statistic.

resDev="string"

names the deviance residual.

resMart="string"

names the martingale residual.

resSch="string"

specifies the prefix for the Schoenfeld residual names. A Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

resSco="string"

specifies the prefix for the score residual names. A score residual name is the concatenation of this prefix with an underscore and the name of a parameter.

role="string"

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

stdXBeta="string"

names the standard error of the linear predictor.

survival="string"

names the survival probability at the observed failure time.

wtResSch="string"

specifies the prefix for the weighted Schoenfeld residual names. A weighted Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

xBeta="string"

names the linear predictor.

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 to use 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" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "FORWARD" \| "LASSO" \| "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="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

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.

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

fast=TRUE | FALSE

implements the computational algorithm of Lawless and Singhal (1978) to compute a first-order approximation to the remaining slope estimates for each subsequent elimination of a variable from the model during backward selection for generalized linear models.

Default	FALSE

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" | "FORWARD" | "LASSO" | "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="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

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="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

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).

ss3=TRUE | FALSE

Default	FALSE

strata="variable-name"

names the variable that identifies the strata for a stratified analysis.

strataMissing=TRUE | FALSE

when set to True, allows missing values as valid STRATA variable values.

Default	FALSE

* table={castable}

specifies 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.

cox Action

Fit Cox regression model.

Lua Syntax
Summary: Input and Output Tables
Parameter Descriptions

Lua Syntax

results, info = s:phreg_cox{

alpha=double,

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

class={{

countMissing=true | false,

descending=true | false,

ignoreMissing=true | false,

levelizeRaw=true | false,

maxLev=integer,

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

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

split=true | false,

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",

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

split=true | false

classLevelsPrint=true | false,

clb=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"

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,

cumHaz=true | false,

fmtWdth=integer,

indentSize=integer,

labelId=integer,

lineSize=integer,

noTrim=true | false,

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

showTime=true | false,

survival=true | false,

tabForm=true | false,

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

collection={{

details=true | false,

name="string",

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

}, {...}},

corrB=true | false,

covB=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

freq="variable-name",

hessian=true | false,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

logLikeNull=true | false,

model={

censor="variable-name",

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

clb=true | false,

depVars={{

name="variable-name"

}, {...}},

effects={{

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

maxInteract=integer,

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

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

}, {...}},

entry="variable-name",

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

informative=true | false,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

offset="variable-name",

ss3=true | false,

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

multimember={{

details=true | false,

name="string",

noEffect=true | false,

stdize=true | false,

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

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

}, {...}},

multipass=true | false,

nClassLevelsPrint=integer,

noStdErr=true | false,

optimization={

absConv=double,

absFConv=double,

absGConv=double,

absXConv=double,

fConv=double,

fConv2=double,

gConv=double,

gConv2=double,

itHist="NONE" | "SUMMARY",

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

xConv=double

output={

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", ...>},

cumHaz="string",

dfBeta="string",

ld="string",

resDev="string",

resMart="string",

resSch="string",

resSco="string",

role="string",

stdXBeta="string",

survival="string",

wtResSch="string",

xBeta="string"

outputTables={

groupByVarsRaw=true | false,

includeAll=true | false,

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

repeated=true | false,

replace=true | false

partByFrac={

seed=integer,

test=double,

validate=double

partByVar={

name="variable-name",

test="string",

train="string",

validate="string"

polynomial={{

degree=integer,

details=true | false,

labelStyle={

expand=true | false

exponent="string"

includeName=true | false

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=true | false,

standardize={

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

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

prefix="NONE" | "string"

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

}, {...}},

selection={

candidates=integer | "ALL",

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,

fast=true | false,

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

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

L2=double,

L2HIGH=double,

L2LOW=double,

maxEffects=integer,

maxSteps=integer,

method="BACKWARD" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE",

minEffects=integer,

plots=true | false,

relaxed=true | false,

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

slEntry=double,

slStay=double,

stopHorizon=integer

spline={{

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

dataBoundary=true | false,

degree=integer,

details=true | false,

knotMax=double,

knotMethod={

equal=integer

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

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

multiscale={

endScale=integer

startScale=integer

}

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

knotMin=double,

name="string",

naturalCubic=true | false,

separate=true | false,

split=true | false,

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

}, {...}},

ss3=true | false,

strata="variable-name",

strataMissing=true | false,

table={

caslib="string",

computedOnDemand=true | false,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

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

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=true | false,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

weight="variable-name"

}

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies input data table

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

Parameter Descriptions

alpha=double

specifies the significance level to use for 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

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
Aliases	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

clb=true | false

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

Default	false

code={coxCodegen}

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

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

casOut={casouttable}

specifies the settings for an output table.

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

caslib="string"

specifies the name of the caslib for the output table.

compress=true | false

when set to True, applies data compression to the table.

Default	false

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

specifies the list of variables to create indexes for in the output data.

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

maxMemSize=64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

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

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

replication=integer

Default	1
Minimum value	0

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.

threadBlockSize=64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

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

comment=true | false

when set to True, adds comments to the DATA step code.

Default	false

cumHaz=true | false

when set to True, generates code for predicting cumulative hazards.

Default	false

fmtWdth=integer

specifies the width to use for formatting derived numbers such as parameter estimates in the DATA step code.

Alias	fmtWidth
Default	20
Range	0–32

indentSize=integer

specifies the number of spaces to indent the DATA step code for each level.

Default	3
Range	0–10

labelId=integer

specifies the label ID to use in array names and statement labels in the DATA step code. By default, a random positive integer is used.

lineSize=integer

specifies the line size for the generated code.

Default	120
Range	64–254

noTrim=true | false

when set to True, bases the comparison of variables with formatted values on the full format width with padding. By default, leading and trailing blanks are removed from the formatted values.

Default	false

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

specifies a list of quantile probabilities. Survival probabilities or cumulative hazards are predicted at the corresponding quantiles.

showTime=true | false

when set to True, generates code for displaying the time points.

Default	false

survival=true | false

when set to True, generates code for predicting survival probabilities

Default	true

tabForm=true | false

when set to True, generates the code in a way that is appropriate for storing in a table.

Alias	tableForm
Default	false

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

specifies a list of time points at which the survival probabilities or cumulative hazards are predicted.

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", ...>}

corrB=true | false

when set to True, displays the correlation matrix of the parameters.

Default	false

covB=true | false

when set to True, displays the covariance matrix of the parameters.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

hessian=true | false

when set to True, uses the analytical Hessian instead of the finite difference Hessian.

Default	false

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

logLikeNull=true | false

when set to True, displays the -2 log likelihood of the NULL model.

Alias	m2ll0
Default	false

model={coxModel}

names the dependent variable, explanatory effects and model options.

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

censor="variable-name"

specifies the censor variable.

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

specifies a list of numeric values that identify censored observations.

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
Aliases	target

name="variable-name"

names the response variable.

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

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

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

entry="variable-name"

specifies the entry variable.

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

informative=true | false

Default	false

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

offset="variable-name"

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

ss3=true | false

Default	false

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

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

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

multipass=true | false

when set to True, levelizes the input data table everytime it is read.

Default	false

nClassLevelsPrint=integer

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

Minimum value	0

noStdErr=true | false

when set to True, does not compute the covariance matrix or any statistic that depends on it.

Default	false

optimization={optimizationStatement}

specifies the technique and options for performing the optimization.

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

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

absConv=double

specifies the absolute function convergence criterion.

Alias	absTol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

absXConv=double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

itHist="NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

NEWRAP

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

NMSIMP

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

NONE

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

NRRIDG

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

TRUREG

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

xConv=double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output={coxOutputStatement}

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

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

* 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", ...>}

cumHaz="string"

names the cumulative hazard at the given time.

dfBeta="string"

specifies the prefix for the dfBeta statistic names. A dfBeta statistic name is the concatenation of this prefix with an underscore and the name of a parameter.

ld="string"

names the likelihood displacement statistic.

resDev="string"

names the deviance residual.

resMart="string"

names the martingale residual.

resSch="string"

specifies the prefix for the Schoenfeld residual names. A Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

resSco="string"

specifies the prefix for the score residual names. A score residual name is the concatenation of this prefix with an underscore and the name of a parameter.

role="string"

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

stdXBeta="string"

names the standard error of the linear predictor.

survival="string"

names the survival probability at the observed failure time.

wtResSch="string"

specifies the prefix for the weighted Schoenfeld residual names. A weighted Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

xBeta="string"

names the linear predictor.

outputTables={outputTables}

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

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

Alias	displayOut

partByFrac={partByFracStatement}

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

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

seed=integer

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

Default	0

test=double

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

Range	0–1

validate=double

Alias	valid
Range	0–1

partByVar={partByVarStatement}

names the variable and its values to use 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"

validate="string"

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

Alias	valid

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

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

Alias	poly

selection={selectionStatement}

specifies the method and options for performing model selection.

Long form	selection={method="BACKWARD" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "FORWARD" \| "LASSO" \| "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="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

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.

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

fast=true | false

Default	false

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

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" | "FORWARD" | "LASSO" | "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="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

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="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

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).

ss3=true | false

Default	false

strata="variable-name"

names the variable that identifies the strata for a stratified analysis.

strataMissing=true | false

when set to True, allows missing values as valid STRATA variable values.

Default	false

* table={castable}

specifies 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.

cox Action

Fit Cox regression model.

Python Syntax
Summary: Input and Output Tables
Parameter Descriptions

Python Syntax

results=s.phreg.cox(

alpha=double,

attributes=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

class_=[{

"countMissing":True | False,

"descending":True | False,

"ignoreMissing":True | False,

"levelizeRaw":True | False,

"maxLev":integer,

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

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

"split":True | False,

"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",

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

"split":True | False

classLevelsPrint=True | False,

clb=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"

"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,

"cumHaz":True | False,

"fmtWdth":integer,

"indentSize":integer,

"labelId":integer,

"lineSize":integer,

"noTrim":True | False,

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

"showTime":True | False,

"survival":True | False,

"tabForm":True | False,

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

collection=[{

"details":True | False,

"name":"string",

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

}<, {...}>],

corrB=True | False,

covB=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

freq="variable-name",

hessian=True | False,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

logLikeNull=True | False,

model={

"censor":"variable-name",

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

"clb":True | False,

"depVars":[{

"name":"variable-name"

}<, {...}>],

"effects":[{

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

"maxInteract":integer,

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

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

}<, {...}>],

"entry":"variable-name",

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

"informative":True | False,

"lassoRho":double,

"lassoSteps":integer,

"lassoTol":double,

"offset":"variable-name",

"ss3":True | False,

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

multimember=[{

"details":True | False,

"name":"string",

"noEffect":True | False,

"stdize":True | False,

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

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

}<, {...}>],

multipass=True | False,

nClassLevelsPrint=integer,

noStdErr=True | False,

optimization={

"absConv":double,

"absFConv":double,

"absGConv":double,

"absXConv":double,

"fConv":double,

"fConv2":double,

"gConv":double,

"gConv2":double,

"itHist":"NONE" | "SUMMARY",

"maxFunc":double,

"maxIter":double,

"maxTime":double,

"minIter":integer,

"xConv":double

output={

"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", ...>],

"cumHaz":"string",

"dfBeta":"string",

"ld":"string",

"resDev":"string",

"resMart":"string",

"resSch":"string",

"resSco":"string",

"role":"string",

"stdXBeta":"string",

"survival":"string",

"wtResSch":"string",

"xBeta":"string"

outputTables={

"groupByVarsRaw":True | False,

"includeAll":True | False,

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

"repeated":True | False,

"replace":True | False

partByFrac={

"seed":integer,

"test":double,

"validate":double

partByVar={

"name":"variable-name",

"test":"string",

"train":"string",

"validate":"string"

polynomial=[{

"degree":integer,

"details":True | False,

"labelStyle":{

"expand":True | False

"exponent":"string"

"includeName":True | False

"productSymbol":"NONE" | "string"

"mDegree":integer,

"name":"string",

"noSeparate":True | False,

"standardize":{

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

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

"prefix":"NONE" | "string"

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

}<, {...}>],

selection={

"candidates":integer | "ALL",

"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,

"fast":True | False,

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

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

"L2":double,

"L2HIGH":double,

"L2LOW":double,

"maxEffects":integer,

"maxSteps":integer,

"method":"BACKWARD" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE",

"minEffects":integer,

"plots":True | False,

"relaxed":True | False,

"select":"AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

"slEntry":double,

"slStay":double,

"stopHorizon":integer

spline=[{

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

"dataBoundary":True | False,

"degree":integer,

"details":True | False,

"knotMax":double,

"knotMethod":{

"equal":integer

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

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

"multiscale":{

"endScale":integer

"startScale":integer

}

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

"knotMin":double,

"name":"string",

"naturalCubic":True | False,

"separate":True | False,

"split":True | False,

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

}<, {...}>],

ss3=True | False,

strata="variable-name",

strataMissing=True | False,

table={

"caslib":"string",

"computedOnDemand":True | False,

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"computedVarsProgram":"string",

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

"groupBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"groupByMode":"NOSORT" | "REDISTRIBUTE",

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

"name":"table-name",

"orderBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

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

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

weight="variable-name"

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies input data table

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

Parameter Descriptions

alpha=double

specifies the significance level to use for 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

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
Aliases	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

clb=True | False

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

Default	False

code={coxCodegen}

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

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

"casOut":{casouttable}

specifies the settings for an output table.

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

"caslib":"string"

specifies the name of the caslib for the output table.

"compress":True | False

when set to True, applies data compression to the table.

Default	False

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

specifies the list of variables to create indexes for in the output data.

"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

"maxMemSize":64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

"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

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

"replication":integer

Default	1
Minimum value	0

"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.

"threadBlockSize":64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

"timeStamp":"string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

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

"comment":True | False

when set to True, adds comments to the DATA step code.

Default	False

"cumHaz":True | False

when set to True, generates code for predicting cumulative hazards.

Default	False

"fmtWdth":integer

specifies the width to use for formatting derived numbers such as parameter estimates in the DATA step code.

Alias	fmtWidth
Default	20
Range	0–32

"indentSize":integer

specifies the number of spaces to indent the DATA step code for each level.

Default	3
Range	0–10

"labelId":integer

specifies the label ID to use in array names and statement labels in the DATA step code. By default, a random positive integer is used.

"lineSize":integer

specifies the line size for the generated code.

Default	120
Range	64–254

"noTrim":True | False

when set to True, bases the comparison of variables with formatted values on the full format width with padding. By default, leading and trailing blanks are removed from the formatted values.

Default	False

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

specifies a list of quantile probabilities. Survival probabilities or cumulative hazards are predicted at the corresponding quantiles.

"showTime":True | False

when set to True, generates code for displaying the time points.

Default	False

"survival":True | False

when set to True, generates code for predicting survival probabilities

Default	True

"tabForm":True | False

when set to True, generates the code in a way that is appropriate for storing in a table.

Alias	tableForm
Default	False

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

specifies a list of time points at which the survival probabilities or cumulative hazards are predicted.

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", ...>]

corrB=True | False

when set to True, displays the correlation matrix of the parameters.

Default	False

covB=True | False

when set to True, displays the covariance matrix of the parameters.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

hessian=True | False

when set to True, uses the analytical Hessian instead of the finite difference Hessian.

Default	False

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

logLikeNull=True | False

when set to True, displays the -2 log likelihood of the NULL model.

Alias	m2ll0
Default	False

model={coxModel}

names the dependent variable, explanatory effects and model options.

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

"censor":"variable-name"

specifies the censor variable.

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

specifies a list of numeric values that identify censored observations.

"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
Aliases	target

"name":"variable-name"

names the response variable.

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

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

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

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

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

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

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

"entry":"variable-name"

specifies the entry variable.

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

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

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

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

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

"informative":True | False

Default	False

"lassoRho":double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

"lassoSteps":integer

specifies the maximum number of steps for the LASSO method.

Default	20

"lassoTol":double

specifies the convergence criterion for the LASSO method.

Default	1E-06

"offset":"variable-name"

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

"ss3":True | False

Default	False

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

"maxInteract":integer

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

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

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

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

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

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

multipass=True | False

when set to True, levelizes the input data table everytime it is read.

Default	False

nClassLevelsPrint=integer

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

Minimum value	0

noStdErr=True | False

when set to True, does not compute the covariance matrix or any statistic that depends on it.

Default	False

optimization={optimizationStatement}

specifies the technique and options for performing the optimization.

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

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

"absConv":double

specifies the absolute function convergence criterion.

Alias	absTol

"absFConv":double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

"absGConv":double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

"absXConv":double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

"fConv":double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

"fConv2":double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

"gConv":double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

"gConv2":double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

"itHist":"NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

"maxFunc":double

specifies the maximum number of function evaluations.

Minimum value	0

"maxIter":double

specifies the maximum number of iterations.

Minimum value	0

"maxTime":double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

"minIter":integer

specifies the minimum number of iterations.

Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

NEWRAP

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

NMSIMP

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

NONE

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

NRRIDG

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

TRUREG

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

"xConv":double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output={coxOutputStatement}

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

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

* "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", ...>]

"cumHaz":"string"

names the cumulative hazard at the given time.

"dfBeta":"string"

specifies the prefix for the dfBeta statistic names. A dfBeta statistic name is the concatenation of this prefix with an underscore and the name of a parameter.

"ld":"string"

names the likelihood displacement statistic.

"resDev":"string"

names the deviance residual.

"resMart":"string"

names the martingale residual.

"resSch":"string"

specifies the prefix for the Schoenfeld residual names. A Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

"resSco":"string"

specifies the prefix for the score residual names. A score residual name is the concatenation of this prefix with an underscore and the name of a parameter.

"role":"string"

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

"stdXBeta":"string"

names the standard error of the linear predictor.

"survival":"string"

names the survival probability at the observed failure time.

"wtResSch":"string"

specifies the prefix for the weighted Schoenfeld residual names. A weighted Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

"xBeta":"string"

names the linear predictor.

outputTables={outputTables}

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

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

Alias	displayOut

partByFrac={partByFracStatement}

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

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

"seed":integer

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

Default	0

"test":double

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

Range	0–1

"validate":double

Alias	valid
Range	0–1

partByVar={partByVarStatement}

names the variable and its values to use 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"

"validate":"string"

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

Alias	valid

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

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

Alias	poly

selection={selectionStatement}

specifies the method and options for performing model selection.

Long form	selection={"method":"BACKWARD" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE"}
Shortcut form	selection="BACKWARD" \| "FORWARD" \| "LASSO" \| "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":"AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

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.

"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

"fast":True | False

Default	False

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

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" | "FORWARD" | "LASSO" | "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":"AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

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":"AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

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).

ss3=True | False

Default	False

strata="variable-name"

names the variable that identifies the strata for a stratified analysis.

strataMissing=True | False

when set to True, allows missing values as valid STRATA variable values.

Default	False

* table={castable}

specifies 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.

cox Action

Fit Cox regression model.

R Syntax
Summary: Input and Output Tables
Parameter Descriptions

R Syntax

results <– cas.phreg.cox(s,

alpha=double,

attributes=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

class=list( list(

countMissing=TRUE | FALSE,

descending=TRUE | FALSE,

ignoreMissing=TRUE | FALSE,

levelizeRaw=TRUE | FALSE,

maxLev=integer,

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

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

split=TRUE | FALSE,

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",

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

split=TRUE | FALSE

classLevelsPrint=TRUE | FALSE,

clb=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"

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,

cumHaz=TRUE | FALSE,

fmtWdth=integer,

indentSize=integer,

labelId=integer,

lineSize=integer,

noTrim=TRUE | FALSE,

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

showTime=TRUE | FALSE,

survival=TRUE | FALSE,

tabForm=TRUE | FALSE,

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

collection=list( list(

details=TRUE | FALSE,

name="string",

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

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

corrB=TRUE | FALSE,

covB=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

freq="variable-name",

hessian=TRUE | FALSE,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

logLikeNull=TRUE | FALSE,

model=list(

censor="variable-name",

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

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", ...>),

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

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

entry="variable-name",

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

informative=TRUE | FALSE,

lassoRho=double,

lassoSteps=integer,

lassoTol=double,

offset="variable-name",

ss3=TRUE | FALSE,

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

multimember=list( list(

details=TRUE | FALSE,

name="string",

noEffect=TRUE | FALSE,

stdize=TRUE | FALSE,

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

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

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

multipass=TRUE | FALSE,

nClassLevelsPrint=integer,

noStdErr=TRUE | FALSE,

optimization=list(

absConv=double,

absFConv=double,

absGConv=double,

absXConv=double,

fConv=double,

fConv2=double,

gConv=double,

gConv2=double,

itHist="NONE" | "SUMMARY",

maxFunc=double,

maxIter=double,

maxTime=double,

minIter=integer,

xConv=double

output=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"

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", ...>),

cumHaz="string",

dfBeta="string",

ld="string",

resDev="string",

resMart="string",

resSch="string",

resSco="string",

role="string",

stdXBeta="string",

survival="string",

wtResSch="string",

xBeta="string"

outputTables=list(

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

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

repeated=TRUE | FALSE,

replace=TRUE | FALSE

partByFrac=list(

seed=integer,

test=double,

validate=double

partByVar=list(

name="variable-name",

test="string",

train="string",

validate="string"

polynomial=list( list(

degree=integer,

details=TRUE | FALSE,

labelStyle=list(

expand=TRUE | FALSE

exponent="string"

includeName=TRUE | FALSE

productSymbol="NONE" | "string"

mDegree=integer,

name="string",

noSeparate=TRUE | FALSE,

standardize=list(

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

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

prefix="NONE" | "string"

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

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

selection=list(

candidates=integer | "ALL",

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,

fast=TRUE | FALSE,

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

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

L2=double,

L2HIGH=double,

L2LOW=double,

maxEffects=integer,

maxSteps=integer,

method="BACKWARD" | "FORWARD" | "LASSO" | "NONE" | "STEPWISE",

minEffects=integer,

plots=TRUE | FALSE,

relaxed=TRUE | FALSE,

select="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL",

slEntry=double,

slStay=double,

stopHorizon=integer

spline=list( list(

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

dataBoundary=TRUE | FALSE,

degree=integer,

details=TRUE | FALSE,

knotMax=double,

knotMethod=list(

equal=integer

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

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

multiscale=list(

endScale=integer

startScale=integer

)

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

knotMin=double,

name="string",

naturalCubic=TRUE | FALSE,

separate=TRUE | FALSE,

split=TRUE | FALSE,

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

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

ss3=TRUE | FALSE,

strata="variable-name",

strataMissing=TRUE | FALSE,

table=list(

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

computedVarsProgram="string",

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

groupBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

orderBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

singlePass=TRUE | FALSE,

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

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

where="where-expression",

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

weight="variable-name"

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies input data table

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

Parameter Descriptions

alpha=double

specifies the significance level to use for 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

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
Aliases	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

clb=TRUE | FALSE

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

Default	FALSE

code=list(coxCodegen)

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

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

casOut=list(casouttable)

specifies the settings for an output table.

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

caslib="string"

specifies the name of the caslib for the output table.

compress=TRUE | FALSE

when set to True, applies data compression to the table.

Default	FALSE

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

specifies the list of variables to create indexes for in the output data.

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

maxMemSize=64-bit-integer

TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

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

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

replication=integer

Default	1
Minimum value	0

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.

threadBlockSize=64-bit-integer

Alias	blockSize
Default	1048576
Minimum value	0
TIP	You can enclose the value in quotation marks and specify B, K, M, G, or T as a suffix to indicate the units. For example, "8M" specifies eight megabytes.

timeStamp="string"

specifies to add a timestamp column to the table. Support for timeStamp is action-specific. Specify the value in the form that is appropriate for your session locale.

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

comment=TRUE | FALSE

when set to True, adds comments to the DATA step code.

Default	FALSE

cumHaz=TRUE | FALSE

when set to True, generates code for predicting cumulative hazards.

Default	FALSE

fmtWdth=integer

specifies the width to use for formatting derived numbers such as parameter estimates in the DATA step code.

Alias	fmtWidth
Default	20
Range	0–32

indentSize=integer

specifies the number of spaces to indent the DATA step code for each level.

Default	3
Range	0–10

labelId=integer

specifies the label ID to use in array names and statement labels in the DATA step code. By default, a random positive integer is used.

lineSize=integer

specifies the line size for the generated code.

Default	120
Range	64–254

noTrim=TRUE | FALSE

when set to True, bases the comparison of variables with formatted values on the full format width with padding. By default, leading and trailing blanks are removed from the formatted values.

Default	FALSE

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

specifies a list of quantile probabilities. Survival probabilities or cumulative hazards are predicted at the corresponding quantiles.

showTime=TRUE | FALSE

when set to True, generates code for displaying the time points.

Default	FALSE

survival=TRUE | FALSE

when set to True, generates code for predicting survival probabilities

Default	TRUE

tabForm=TRUE | FALSE

when set to True, generates the code in a way that is appropriate for storing in a table.

Alias	tableForm
Default	FALSE

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

specifies a list of time points at which the survival probabilities or cumulative hazards are predicted.

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", ...>)

corrB=TRUE | FALSE

when set to True, displays the correlation matrix of the parameters.

Default	FALSE

covB=TRUE | FALSE

when set to True, displays the covariance matrix of the parameters.

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).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

hessian=TRUE | FALSE

when set to True, uses the analytical Hessian instead of the finite difference Hessian.

Default	FALSE

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

logLikeNull=TRUE | FALSE

when set to True, displays the -2 log likelihood of the NULL model.

Alias	m2ll0
Default	FALSE

model=list(coxModel)

names the dependent variable, explanatory effects and model options.

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

censor="variable-name"

specifies the censor variable.

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

specifies a list of numeric values that identify censored observations.

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
Aliases	target

name="variable-name"

names the response variable.

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

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

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

entry="variable-name"

specifies the entry variable.

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

informative=TRUE | FALSE

Default	FALSE

lassoRho=double

specifies the base regularization parameter for the LASSO method.

Default	0.8
Range	(0, 1)

lassoSteps=integer

specifies the maximum number of steps for the LASSO method.

Default	20

lassoTol=double

specifies the convergence criterion for the LASSO method.

Default	1E-06

offset="variable-name"

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

ss3=TRUE | FALSE

Default	FALSE

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

The effect value is specified as follows:

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

specifies the type of interaction for the variables.

Alias	interact
Default	NONE

maxInteract=integer

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

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

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

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

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

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

multipass=TRUE | FALSE

when set to True, levelizes the input data table everytime it is read.

Default	FALSE

nClassLevelsPrint=integer

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

Minimum value	0

noStdErr=TRUE | FALSE

when set to True, does not compute the covariance matrix or any statistic that depends on it.

Default	FALSE

optimization=list(optimizationStatement)

specifies the technique and options for performing the optimization.

Long form	optimization=list(technique="CONGRA" \| "DBLDOG" \| "DUQUANEW" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG")
Shortcut form	optimization="CONGRA" \| "DBLDOG" \| "DUQUANEW" \| "NEWRAP" \| "NMSIMP" \| "NONE" \| "NRRIDG" \| "QUANEW" \| "TRUREG"

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

absConv=double

specifies the absolute function convergence criterion.

Alias	absTol

absFConv=double

specifies the absolute function difference convergence criterion.

Alias	absFTol
Minimum value	0

absGConv=double

specifies the absolute gradient convergence criterion.

Alias	absGTol
Minimum value	0

absXConv=double

specifies the absolute parameter convergence criterion.

Alias	absXTol
Minimum value	0

fConv=double

specifies the relative function difference convergence criterion.

Alias	fTol
Minimum value	0

fConv2=double

specifies the second relative function difference convergence criterion.

Alias	fTol2
Minimum value	0

gConv=double

specifies the relative gradient convergence criterion.

Alias	gTol
Minimum value	0

gConv2=double

specifies the second relative gradient convergence criterion.

Alias	gTol2
Minimum value	0

itHist="NONE" | "SUMMARY"

controls the display of the iteration history.

Default	SUMMARY

NONE

suppresses the iteration history.

SUMMARY

displays the iteration history.

maxFunc=double

specifies the maximum number of function evaluations.

Minimum value	0

maxIter=double

specifies the maximum number of iterations.

Minimum value	0

maxTime=double

specifies the maximum allowed CPU time in seconds.

Minimum value	0

minIter=integer

specifies the minimum number of iterations.

Minimum value	0

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

specifies the optimization technique.

CONGRA

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

DBLDOG

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

DUQUANEW

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

Alias	QUANEW

NEWRAP

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

NMSIMP

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

NONE

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

NRRIDG

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

TRUREG

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

xConv=double

specifies the relative parameter convergence criterion.

Alias	xTol
Minimum value	0

output=list(coxOutputStatement)

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

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

* 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", ...>)

cumHaz="string"

names the cumulative hazard at the given time.

dfBeta="string"

specifies the prefix for the dfBeta statistic names. A dfBeta statistic name is the concatenation of this prefix with an underscore and the name of a parameter.

ld="string"

names the likelihood displacement statistic.

resDev="string"

names the deviance residual.

resMart="string"

names the martingale residual.

resSch="string"

specifies the prefix for the Schoenfeld residual names. A Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

resSco="string"

specifies the prefix for the score residual names. A score residual name is the concatenation of this prefix with an underscore and the name of a parameter.

role="string"

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

stdXBeta="string"

names the standard error of the linear predictor.

survival="string"

names the survival probability at the observed failure time.

wtResSch="string"

specifies the prefix for the weighted Schoenfeld residual names. A weighted Schoenfeld residual name is the concatenation of this prefix with an underscore and the name of a parameter.

xBeta="string"

names the linear predictor.

outputTables=list(outputTables)

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

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

Alias	displayOut

partByFrac=list(partByFracStatement)

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

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

seed=integer

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

Default	0

test=double

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

Range	0–1

validate=double

Alias	valid
Range	0–1

partByVar=list(partByVarStatement)

names the variable and its values to use 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"

validate="string"

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

Alias	valid

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

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

Alias	poly

selection=list(selectionStatement)

specifies the method and options for performing model selection.

Long form	selection=list(method="BACKWARD" \| "FORWARD" \| "LASSO" \| "NONE" \| "STEPWISE")
Shortcut form	selection="BACKWARD" \| "FORWARD" \| "LASSO" \| "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="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "VALIDATE"

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.

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

fast=TRUE | FALSE

Default	FALSE

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

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" | "FORWARD" | "LASSO" | "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="AIC" | "AICC" | "DEFAULT" | "SBC" | "SL"

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="AIC" | "AICC" | "DEFAULT" | "NONE" | "SBC" | "SL" | "VALIDATE"

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).

ss3=TRUE | FALSE

Default	FALSE

strata="variable-name"

names the variable that identifies the strata for a stratified analysis.

strataMissing=TRUE | FALSE

when set to True, allows missing values as valid STRATA variable values.

Default	FALSE

* table=list(castable)

specifies 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