
ABSFCONV=r
ABSFTOL=r

specifies an absolute function difference convergence criterion. Termination requires a small change of the function value
in successive iterations,
where denotes the vector of parameters that participate in the optimization and is the objective function. This criterion is not used by the expectationmaximization (EM) algorithm. By default, r = 0.

ABSGCONV=r
ABSGTOL=r

specifies an absolute gradient convergence criterion. Termination requires the maximum absolute gradient element to be small,
where denotes the vector of parameters that participate in the optimization and is the gradient of the objective function with respect to the jth parameter. This criterion is not used by the EM algorithm. By default, r = 1E–5.

ABSPCONV=r
ABSPTOL=r

specifies a maximum absolute parameter difference convergence criterion. This criterion is used only by the EM algorithm.
Termination requires the maximum absolute parameter change in successive iterations to be small,
where denotes the jth parameter that participates in the optimization. By default, r = 1E–4.

DATA=SASdataset

specifies the SASdataset to be read by PROC IRT. The default value is the most recently created data set.

DESCENDING
DESC

reverses the sorting order for the levels of the response variables. If you specify both the DESCENDING and RORDER= options,
PROC IRT orders the levels according to the RORDER= option and then reverses that order.

FCONV=r
FTOL=r

specifies a relative function convergence criterion. Termination requires a small relative change of the function value in
successive iterations,
where denotes the vector of parameters that participate in the optimization and is the objective function. This criterion is not used by the EM algorithm. By default, r , where FDIGITS is, by default, and is the machine precision.

GCONV=r
GTOL=r

specifies a relative gradient convergence criterion. For all techniques except CONGRA, termination requires the normalized
predicted function reduction to be small,
where denotes the vector of parameters that participate in the optimization, is the objective function, and is the gradient. For the CONGRA technique (for which a reliable Hessian estimate is not available), the following criterion is used:
This criterion is not used by the EM algorithm. By default, r = 1E–8.

INMODEL<(SCORE)>=SASdataset

specifies an input data set that contains information about the analysis model. Instead of specifying and running the model
in a new run, you can use the INMODEL= option to input the model specification saved as an OUTMODEL= data set in a previous
PROC IRT run.
Sometimes, you might want to create an INMODEL= data set by modifying an existing OUTMODEL= data set. However, editing and
modifying OUTMODEL= data sets requires a good understanding of the formats and contents of the OUTMODEL= data sets. This process
could be difficult for novice users. For more information about the format of INMODEL= and OUTMODEL= data sets, see the sectionOutput Data Sets.
When you specify the INMODEL= option, the VAR, MODEL, GROUP, FACTOR, VARIANCE, COV, and EQUALITY statements are ignored. The
DESCENDING, LINK, NFACTOR, RESFUNC, and RORDER options in the PROC IRT statement are also ignored. When there are duplicated
specifications, the first specification is used.
Specify the SCORE suboption if you want to use the model specifications and parameter estimates from the INMODEL= data set
to score a new subject without refitting the model.
You can use the INMODEL= option along with the SCORE suboption for many different purposes, including the following:

If you specify the INMODEL= option, PROC IRT fits an IRT model to the DATA=
data set based on the model specifications in the INMODEL= data set and uses the parameter estimates in the INMODEL= data
set as initial values.

If you specify the INMODEL= option and the OUT= option, PROC IRT fits an IRT model to the DATA=
data set based on the model specifications in the INMODEL= data set and uses the parameter estimates in the INMODEL= data
set as initial values. Then PROC IRT scores the DATA=
data set by using the new parameter estimates obtained in the previous step.

If you specify the INMODEL(SCORE)= option and the OUT= option, PROC IRT scores the DATA=
data set by using the model specifications and parameter estimates in the INMODEL= data set without refitting the model.

ITEMFIT

displays the item fit statistics. These item fit statistics apply only to binary items that have one latent factor.

ITEMSTAT <(itemstatoptions )>

displays the classical item statistics, which include the item means, itemtotal correlations, adjusted itemtotal correlations,
and item means for i ordered groups of observations or individuals. You can specify the following itemstatoptions:

NPARTITION=i

specifies the number of groups, where i must be an integer between 2 and 5, inclusive. By default NPARTITION=4.
The i ordered groups are formed by partitioning subjects based on the rank of their sum scores. By default, there are four groups,
labeled G1, G2, G3, and G4, representing four ascending ranges of sum scores. The formula for calculating group values is
where floor is the floor function, rank is the sum score’s order rank, i is the value of the NPARTITION= option, and n is the number of observations that have nonmissing values of sum scores for TIES=LOW, TIES=MEAN, and TIES=HIGH. For TIES=DENSE,
n is the number of observations that have unique nonmissing sum scores. If the number of observations is evenly divisible by
the number of groups, each group has the same number of observations, provided that there are no tied sum scores at the boundaries
of the groups. Sum scores with many tied values can create unbalanced groups because observations that have the same sum scores
are assigned to the same group.

TIES=HIGH  LOW  MEAN  DENSE

specifies how to compute normal scores or ranks for tied data values.
 HIGH

assigns the largest of the corresponding ranks.
 LOW

assigns the smallest of the corresponding ranks.
 MEAN

assigns the mean of the corresponding rank.
 DENSE

computes scores and ranks by treating tied values as a singleorder statistic. For the default method, ranks are consecutive
integers that begin with the number 1 and end with the number of unique, nonmissing values of the variable that is being ranked.
Tied values are assigned the same rank.
By default, TIES=MEAN.
Observations (subjects) that have missing values are excluded from the computations of the classical item statistics.

LINK=name

specifies the link function. You can specify the following names:
 LOGIT

requests the logistic link function.
 PROBIT

requests the probit link function.
By default, LINK=LOGIT.

MAXFUNC=n
MAXFU=n

specifies the maximum number of function calls in the optimization process. This option is not used by the EM algorithm. The
default values are as follows, depending on which optimization technique is specified in the TECHNIQUE=
option:

NRRIDG: 125

QUANEW: 500

CONGRA: 1000
The optimization can terminate only after completing a full iteration. Therefore, the number of function calls that are actually
performed can exceed the number that this option specifies.

MAXITER=n
MAXIT=n

specifies the maximum number of iterations in the optimization process. The default values are as follows, depending on which
optimization technique is specified in the TECHNIQUE=
option:

NRRIDG: 50

QUANEW: 200

CONGRA: 400

EM: 500

MAXMITER=n
MAXMIT=n

specifies the maximum number of iterations in the maximization step of the EM algorithm. By default, MAXMITER=1.

NFACTOR=i
NFACT=i

specifies the number of factors, i, in the model. You must specify the number of factors only for exploratory analysis, in which all the slope parameters of
the items are freely estimated without being explicitly constrained by using the FACTOR
statement. By default, NFACTOR=1. When you use the FACTOR
statement to specify the confirmatory factor pattern, the number of factors is implicitly defined by the number of distinctive
factor names that you specify in the statement.

NOAD

requests that the Gaussian quadrature be nonadaptive.

NOITPRINT

suppresses the display of the "Iteration History" table.

NOPRINT

suppresses all output displays.

OUT=SASdataset

creates an output data set that contains all the data in the DATA=
data set plus estimated factor scores. For exploratory analysis, the factor scores are named _Factor1
, _Factor2
, and so on. For confirmatory analysis, userspecified factor names are used.
PROC IRT provides three estimation methods for factor scores. You can specify a method by using the SCOREMETHOD
option. The default estimation method, maximum a posteriori (MAP), is used if the SCOREMETHOD
option is not specified.

OUTMODEL=SASdataset

creates an output data set that contains the model specification, the parameter estimates, and their standard errors. You
can use an OUTMODEL= data set as an input INMODEL=
data set in a subsequent analysis by PROC IRT.
If you want to create a SAS data set in a permanent library, you must specify a twolevel name. For more information about
permanent libraries and SAS data sets, see
SAS Language Reference: Concepts.

PINITIAL

displays the initial parameter estimates.

PLOTS <(globalplotoptions)> <= plotrequest <(options)>>
PLOTS <(globalplotoptions)> <= (plotrequest <(options)> <…plotrequest <(options)>>)>

controls the plots that are produced through ODS Graphics. When you specify only one plotrequest, you can omit the parentheses around it. For example:
plots=all
plots=ICC(unpack)
plots(unpack)=(scree ICC)
ODS Graphics must be enabled before plots can be requested. For example:
ods graphics on;
proc irt plots=all;
run;
ods graphics off;
For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.
You can specify the following globalplotoptions, which apply to all plots that the IRT procedure generates:

UNPACK UNPACKPANEL

suppresses paneling. By default, multiple plots can appear in some output panels. Specify UNPACK to display each plot individually.
You can also specify UNPACK as a suboption in the ICC, IIC, and SCREE options.

XVIEWMAX

specifies a maximum value for the X axis. You can also specify XVIEWMAX as a suboption in the ICC, IIC, and TIC options.

XVIEWMIN

specifies a minimum value for the X axis. You can also specify XVIEWMIN as a suboption in the ICC, IIC, and TIC options.
You can specify the following plotrequests:

ALL

displays all default plots.

ICC <(UNPACK UNPACKPANEL), (XVIEWMAX=), (XVIEWMIN=)>

displays item characteristic curves (ICCs). By default, multiple ICC plots appear in some output panels. You can request an
individual ICC plot for each item by specifying the UNPACK suboption. For binary items, the ICC plot includes only the curve
for the higher category, which is often the correct response category or the endorsed category. For ordinal items that have
more than two categories, the ICC plot includes curves for all the categories and also a legend with the values 0, 1, 2, and
so on to indicate the curves for different categories.

IIC <(UNPACK UNPACKPANEL), (XVIEWMAX=), (XVIEWMIN=)>

displays item information curves (IICs). By default, multiple IIC plots appear in some output panels. You can request an individual
IIC plot for each item by specifying the UNPACK suboption.

NONE

suppresses all plots.

POLYCHORIC <options>
PLCORR<options>

displays a heat map of the polychoric correlation matrix. You can specify one or both of the following options:

FUZZ=p

displays polychoric correlations whose absolute values are less than p as 0 in the heat map. This option is useful when you want to focus on the patterns of sizable correlations that are larger
than p in the heat map. By default, FUZZ=0.

OUTLINE=ON  OFF

specifies whether to display an outline of the regions in the polychoric correlation heat map. By default, OUTLINE=ON.

SCREE <(UNPACK UNPACKPANEL)>

displays the scree and varianceexplained plots in the same panel. You can display these plots individually by specifying
the UNPACK suboption.

TIC <(XVIEWMAX=), (XVIEWMIN=)>

displays a test information curve (TIC) plot.

POLYCHORIC

displays the polychoric correlation matrix.

QPOINTS=i

specifies the number of quadrature points in each dimension of the integral. If there are d latent factors and n quadrature points, the IRT procedure evaluates conditional log likelihoods for each observation to compute one value of the objective function. Increasing the number of
quadrature nodes can substantially increase the computational burden. If you do not specify the number of quadrature points,
it is determined adaptively by using the initial parameter estimates.

RCONVERGE=p
RCONV=p

specifies the convergence criterion for rotation cycles. Rotation stops when the scaled change of the simplicity function
value is less than the RCONVERGE= value. The default convergence criterion is
where and are simplicity function values
of the current cycle and the previous cycle, respectively; is a scaling factor; and is 1E–9 by default and is modified by the RCONVERGE= value.

RESFUNC=ONEP  TWOP  THREEP  FOURP  GRADED  RASCH

specifies the response functions for the variables that are included in the VAR
statement. The response functions correspond to different response models. You can specify the following values:
 ONEP

specifies the oneparameter model.
 TWOP

specifies the twoparameter model.
 THREEP

specifies the threeparameter model.
 FOURP

specifies the fourparameter model.
 GRADED

specifies the graded response model.
 RASCH

specifies the Rasch model.
By default, RESFUNC=TWOP for binary items and RESFUNC=GRADED for ordinal items. The graded response model assumes that the
response variables are ordinalcategorical up to 11 levels. All other models assume binary responses. For more information
about these response models, see "Response Models" in the Details: IRT Procedure section.

RITER=n

specifies the maximum number of cycles for factor rotation. The default value is the maximum between 10 times the number of
variables and 100.

RORDER=DATA  FORMATTED  FREQ  INTERNAL

specifies the sort order for the levels of the response variable. This order determines which threshold parameter in the model
corresponds to each level in the data. If RORDER=FORMATTED for numeric variables for which you have supplied no explicit format,
the levels are ordered by their internal values. This option applies to all the responses in the model. When the default,
RORDER=FORMATTED, is in effect for numeric variables for which you have supplied no explicit format, the levels are ordered
by their internal values. You can specify the following sort orders:
Value of RORDER=

Levels Sorted By

DATA

Order of appearance in the input data set

FORMATTED

External formatted value, except for numeric variables that have no explicit format, which are sorted by their unformatted
(internal) value

FREQ

Descending frequency count; levels that contain the most observations come first in the order

INTERNAL

Unformatted value

For FORMATTED and INTERNAL, the sort order is machinedependent. For more information about sort order, see the chapter on
the SORT procedure in the SAS Procedures Guide and the discussion of BYgroup processing in
SAS Language Reference: Concepts.

ROTATE=name
R=name

specifies the rotation method.
You can specify the following orthogonal rotation methods:
 BIQUARTIMAX  BIQMAX

specifies orthogonal biquartimax rotation.
 EQUAMAX  E

specifies orthogonal equamax rotation.
 NONE  N

specifies that no rotation be performed, leaving the original orthogonal solution.
 PARSIMAX  PA

specifies orthogonal parsimax rotation.
 QUARTIMAX  QMAX  Q

specifies orthogonal quartimax rotation.
 VARIMAX  V

specifies orthogonal varimax rotation.
You can specify the following oblique rotation methods:
 BIQUARTIMIN  BIQMIN

specifies biquartimin rotation.
 COVARIMIN  CVMIN

specifies covarimin rotation.
 OBBIQUARTIMAX  OBIQMAX

specifies oblique biquartimax rotation.
 OBEQUAMAX  OE

specifies oblique equamax rotation.
 OBPARSIMAX  OPA

specifies oblique parsimax rotation.
 OBQUARTIMAX  OQMAX

specifies oblique quartimax rotation.
 OBVARIMAX  OV

specifies oblique varimax rotation.
 QUARTIMIN  QMIN

specifies quartimin rotation.
By default, ROTATE=VARIMAX.

SCOREMETHOD=ML  EAP  MAP

specifies the method of factor score estimation. You can specify the following methods:
 ML

requests the maximum likelihood method.
 EAP

requests the expected a posteriori method.
 MAP

requests the maximum a posteriori method.
By default, SCOREMETHOD=MAP.

TECHNIQUE=CONGRA  EM  NONE  NRRIDG  QUANEW
TECH=CONGRA  EM  NONE  NRRIDG  QUANEW
OMETHOD=CONGRA  EM  NONE  NRRIDG  QUANEW

specifies the optimization technique to obtain maximum likelihood estimates. You can specify the following techniques:
 CONGRA

performs a conjugategradient optimization.
 EM

performs an EM optimization.
 NONE

performs no optimization.
 NRRIDG

performs a NewtonRaphson optimization with ridging.
 QUANEW

performs a dual quasiNewton optimization.
By default, TECHNIQUE=QUANEW.
For more information about these optimization methods (except EM), see the section Choosing an Optimization Algorithm in Chapter 19: Shared Concepts and Topics. For more information about the EM algorithm, see "ExpectationMaximization (EM) Algorithm" in the section Details: IRT Procedure.