This function extracts various internal objects from an object of class est_irt
or est_item
.
getirt(x, ...)# S3 method for est_irt
getirt(x, what, ...)
# S3 method for est_item
getirt(x, what, ...)
Further arguments passed to or from other methods.
A character string indicating what to extract.
Objects which can be extracted from the object of class est_irt include:
A data frame containing both the item parameter estimates and the corresponding standard errors of estimates.
A data frame containing the item parameter estimates.
A data frame containing the standard errors of the item parameter estimates. Note that the standard errors are estimated using observed information functions. The standard errors are estimated using the cross-production approximation method (Meilijson, 1989).
A data frame containing the position number of item parameters being estimated. The position information is useful when interpreting the variance-covariance matrix of item parameter estimates.
A matrix of variance-covariance matrix of item parameter estimates.
A sum of the log-likelihood values of the observed data set (marginal log-likelihood) across all estimated items.
A model fit statistic of Akaike information criterion based on the loglikelihood.
A model fit statistic of Bayesian information criterion based on the loglikelihood.
A data frame containing the mean, variance, and standard deviation of latent variable prior distribution.
A two-column matrix or data frame containing the quadrature points (in the first column) and the corresponding weights (in the second column) of the (updated) latent variable prior distribution.
A matrix of normalized posterior densities for all the response patterns at each of the quadrature points. The row and column indicate the response pattern and the quadrature point, respectively.
A data frame of the examinees' response data set.
A scaling factor in IRT models.
A total number of response patterns.
A total number of items included in the response data.
A convergence criteria for E steps of the EM algorithm.
The maximum number of E steps in the EM algorithm.
A list containing the information of the prior distribution for item slope parameters.
A list containing the information of the prior distribution for item difficulty (or threshold) parameters.
A list containing the information of the prior distribution for item guessing parameters.
A total number of the estimated parameters.
The number of EM cycles completed.
A maximum item parameter change when the EM cycles were completed.
Time (in seconds) spent for the EM cycles.
Time (in seconds) spent for computing the standard errors of the item parameter estimates.
Time (in seconds) spent for total compuatation.
Status of the first-order test to report if the gradients has vanished sufficiently for the solution to be stable.
Status of the second-order test to report if the information matrix is positive definite, which is a prerequisite for the solution to be a possible maximum.
A note to report if the variance-covariance matrix of item parameter estimates is obtainable from the information matrix.
A logical value to indicate if FIPC was used.
A method used for the FIPC.
A vector of integer values specifying the location of the fixed items when the FIPC was implemented.
Objects which can be extracted from the object of class est_item include:
A data frame containing both the item parameter estimates and the corresponding standard errors of estimates.
A data frame containing the item parameter estimates.
A data frame containing the standard errors of the item parameter estimates. Note that the standard errors are estimated using observed information functions.
A sum of the log-likelihood values of the complete data set across all estimated items.
A data frame of the examinees' response data set.
A vector of the examinees' ability values used as the fixed effects.
A scaling factor in IRT models.
A string indicating the convergence status of the item parameter estimation.
A total number of items included in the response data.
The items which have no item response data. Those items are excluded from the item parameter estimation.
A total number of the estimated parameters.
An integer vector indicating the number of item responses for each item used to estimate the item parameters.
Time (in seconds) spent for total compuatation.
# NOT RUN {
# fit the 2PL model to LSAT6 data
mod.2pl <- est_irt(data=LSAT6, D=1, model="2PLM", cats=2)
# extract the item parameter estimates
(est.par <- getirt(mod.2pl, what="par.est"))
# extract the standard error estimates
(est.se <- getirt(mod.2pl, what="se.est"))
# extract the variance-covariance matrix of item parameter estimates
(cov.mat <- getirt(mod.2pl, what="covariance"))
# }
# NOT RUN {
# }
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