Estimates a user defined item response model. Both, item response functions and latent trait distributions can be specified by the user (see Details).
xxirt(dat, Theta=NULL, itemtype=NULL, customItems=NULL, partable=NULL,
customTheta=NULL, group=NULL, weights=NULL, globconv=1e-06, conv=1e-04,
maxit=1000, mstep_iter=4, mstep_reltol=1e-06, h=1E-4, use_grad=TRUE,
verbose=TRUE, penalty_fun_item=NULL, verbose_index=NULL)# S3 method for xxirt
summary(object, digits=3, file=NULL, ...)
# S3 method for xxirt
print(x, ...)
# S3 method for xxirt
anova(object,...)
# S3 method for xxirt
coef(object,...)
# S3 method for xxirt
logLik(object,...)
# S3 method for xxirt
vcov(object,...)
# S3 method for xxirt
confint(object, parm, level=.95, ... )
# S3 method for xxirt
IRT.expectedCounts(object,...)
# S3 method for xxirt
IRT.factor.scores(object, type="EAP", ...)
# S3 method for xxirt
IRT.irfprob(object,...)
# S3 method for xxirt
IRT.likelihood(object,...)
# S3 method for xxirt
IRT.posterior(object,...)
# S3 method for xxirt
IRT.modelfit(object,...)
# S3 method for IRT.modelfit.xxirt
summary(object,...)
# S3 method for xxirt
IRT.se(object,...)
# computes Hessian matrix
xxirt_hessian(object)
List with following entries
Item parameter table
Vector with estimated item parameters
Data frame with item parameters
Data frame with summary on bounds of estimated item parameters
Vector with estimated parameters of theta distribution
Matrix with \(\bold{\theta}\) grid
Item response functions
Theta distribution
Deviance
Log likelihood value
Information criteria
List with item functions
Used customized item response functions
Used customized theta distribution
Individual likelihood
Individual posterior
Case-wise log-likelihood values
Array of expected counts
EAP person parameter estimates
Used dataset with item responses
Dataset with response indicators
Vector of person weights
Number of groups
Integer vector of group indicators
Vector of original group_identifiers
Number of categories per item
Logical whether model has converged
Number of iterations needed
Data frame with item responses
Matrix with \(\bold{\theta}\) grid vector of latent trait
Vector of item types
List containing types of item response functions created by
xxirt_createDiscItem.
Item parameter table which is initially created by
xxirt_createParTable and which can be modified by
xxirt_modifyParTable.
User defined \(\bold{\theta}\) distribution created by
xxirt_createThetaDistribution.
Optional vector of group indicators
Optional vector of person weights
Convergence criterion for relative change in deviance
Convergence criterion for absolute change in parameters
Maximum number of iterations
Maximum number of iterations in M-step
Convergence criterion in M-step
Numerical differentiation parameter
Logical indicating whether the gradient should be supplied
to stats::optim
Logical indicating whether iteration progress should be displayed
Optional penalty function used in regularized estimation
Object of class xxirt
Number of digits to be rounded
Optional file name to which summary output is written
Optional vector of parameters
Confidence level
Logical indicating whether item index should be printed in estimation output
Object of class xxirt
Type of person parameter estimate. Currently, only
EAP is implemented.
Further arguments to be passed
Item response functions can be specified as functions of unknown parameters \(\bold{\delta}_i\) such that \(P(X_{i}=x | \bold{\theta})=f_i( x | \bold{\theta} ; \bold{\delta}_i )\) The item response model is estimated under the assumption of local stochastic independence of items. Equality constraints of item parameters \(\bold{\delta}_i\) among items are allowed.
The probability distribution \(P(\bold{\theta})\) are specified as functions of an unknown parameter vector \(\bold{\gamma}\).
A penalty function for item parameters can be specified in
penalty_fun_item. The penalty function should be differentiable and
a non-differentiable function (e.g., the absolute value function) should
be approximated by a differentiable function.
See the mirt::createItem and
mirt::mirt functions in the mirt
package for similar functionality.