getirt {irtQ}R Documentation

Extract Components from 'est_irt', 'est_mg', or 'est_item' Objects

Description

Extracts internal components from an object of class est_irt (from est_irt()), est_mg (from est_mg()), or est_item (from est_item()).

Usage

getirt(x, ...)

## S3 method for class 'est_irt'
getirt(x, what, ...)

## S3 method for class 'est_mg'
getirt(x, what, ...)

## S3 method for class 'est_item'
getirt(x, what, ...)

Arguments

x

An object of class est_irt, est_mg, or est_item as returned by est_irt(), est_mg(), or est_item(), respectively.

...

Additional arguments passed to or from other methods.

what

A character string specifying the name of the internal component to extract.

Details

The following components can be extracted from an object of class est_irt created by est_irt():

estimates

A data frame containing both the item parameter estimates and their corresponding standard errors.

par.est

A data frame containing only the item parameter estimates.

se.est

A data frame containing the standard errors of the item parameter estimates, calculated using the cross-product approximation method (Meilijson, 1989).

pos.par

A data frame indicating the position index of each estimated item parameter. This is useful when interpreting the variance-covariance matrix.

covariance

A variance-covariance matrix of the item parameter estimates.

loglikelihood

The total marginal log-likelihood value summed across all items.

aic

Akaike Information Criterion (AIC) based on the marginal log-likelihood.

bic

Bayesian Information Criterion (BIC) based on the marginal log-likelihood.

group.par

A data frame containing the mean, variance, and standard deviation of the latent variable's prior distribution.

weights

A two-column data frame containing quadrature points (first column) and corresponding weights (second column) of the (updated) latent trait prior.

posterior.dist

A matrix of normalized posterior densities for all response patterns at each quadrature point. Rows represent examinees, and columns represent quadrature points.

data

A data frame of the examinee response dataset used in estimation.

scale.D

The scaling constant (usually 1 or 1.7) used in the IRT model.

ncase

The number of unique response patterns.

nitem

The number of items included in the dataset.

Etol

The convergence criterion used for the E-step in the EM algorithm.

MaxE

The maximum number of E-steps allowed during EM estimation.

aprior

A list describing the prior distribution for item slope parameters.

bprior

A list describing the prior distribution for item difficulty (or threshold) parameters.

gprior

A list describing the prior distribution for item guessing parameters.

npar.est

The total number of parameters estimated.

niter

The number of EM cycles completed.

maxpar.diff

The maximum change in parameter estimates at convergence.

EMtime

Computation time (in seconds) for the EM algorithm.

SEtime

Computation time (in seconds) for estimating standard errors.

TotalTime

Total computation time (in seconds) for model estimation.

test.1

Result of the first-order test indicating whether the gradients were sufficiently close to zero.

test.2

Result of the second-order test indicating whether the information matrix was positive definite (a condition for maximum likelihood).

var.note

A note indicating whether the variance-covariance matrix was successfully derived from the information matrix.

fipc

Logical value indicating whether Fixed Item Parameter Calibration (FIPC) was applied.

fipc.method

The specific method used for FIPC.

fix.loc

An integer vector indicating the positions of fixed items used during FIPC.

Components that can be extracted from an object of class est_mg created by est_mg() include:

estimates

A list with two components: overall and group.

  • overall: A data frame containing item parameter estimates and their standard errors, based on the combined data set across all groups.

  • group: A list of group-specific data frames containing item parameter estimates and standard errors for each group.

par.est

Same structure as estimates, but containing only the item parameter estimates (without standard errors).

se.est

Same structure as estimates, but containing only the standard errors of the item parameter estimates. The standard errors are computed using the cross-product approximation method (Meilijson, 1989).

pos.par

A data frame indicating the position index of each estimated parameter. This index is based on the combined item set across all groups and is useful when interpreting the variance-covariance matrix.

covariance

A variance-covariance matrix for the item parameter estimates based on the combined data from all groups.

loglikelihood

A list with overall and group components:

  • overall: The marginal log-likelihood summed over all unique items across all groups.

  • group: Group-specific marginal log-likelihood values.

aic

Akaike Information Criterion (AIC) computed from the overall log-likelihood.

bic

Bayesian Information Criterion (BIC) computed from the overall log-likelihood.

group.par

A list of group-specific summary statistics (mean, variance, and standard deviation) of the latent trait prior distribution.

weights

A list of two-column data frames (one per group) containing the quadrature points (first column) and the corresponding weights (second column) for the updated prior distributions.

posterior.dist

A matrix of normalized posterior densities for all response patterns at each quadrature point. Rows correspond to individuals, and columns to quadrature points.

data

A list with overall and group components, each containing examinee response data.

scale.D

The scaling constant used in the IRT model (typically 1 or 1.7).

ncase

A list with overall and group components indicating the number of response patterns in each.

nitem

A list with overall and group components indicating the number of items in the respective response sets.

Etol

Convergence criterion used for the E-step in the EM algorithm.

MaxE

Maximum number of E-steps allowed in the EM algorithm.

aprior

A list describing the prior distribution for item slope parameters.

gprior

A list describing the prior distribution for item guessing parameters.

npar.est

Total number of parameters estimated across all unique items.

niter

Number of EM cycles completed.

maxpar.diff

Maximum change in item parameter estimates at convergence.

EMtime

Computation time (in seconds) for EM estimation.

SEtime

Computation time (in seconds) for estimating standard errors.

TotalTime

Total computation time (in seconds) for model estimation.

test.1

First-order condition test result indicating whether gradients converged sufficiently.

test.2

Second-order condition test result indicating whether the information matrix is positive definite.

var.note

A note indicating whether the variance-covariance matrix was successfully derived from the information matrix.

fipc

Logical value indicating whether Fixed Item Parameter Calibration (FIPC) was used.

fipc.method

The method used for FIPC.

fix.loc

A list with overall and group components specifying the locations of fixed items when FIPC was applied.

Components that can be extracted from an object of class est_item created by est_item() include:

estimates

A data frame containing both the item parameter estimates and their corresponding standard errors.

par.est

A data frame containing only the item parameter estimates.

se.est

A data frame containing the standard errors of the item parameter estimates, computed using observed information functions.

pos.par

A data frame indicating the position index of each estimated item parameter. This is useful when interpreting the variance-covariance matrix.

covariance

A variance-covariance matrix of the item parameter estimates.

loglikelihood

The sum of log-likelihood values across all items in the complete data set.

data

A data frame of examinee response data.

score

A numeric vector of examinees' ability values used as fixed effects during estimation.

scale.D

The scaling constant (typically 1 or 1.7) used in the IRT model.

convergence

A character string indicating the convergence status of the item parameter estimation.

nitem

The total number of items included in the response data.

deleted.item

Items that contained no response data and were excluded from estimation.

npar.est

The total number of estimated item parameters.

n.response

An integer vector indicating the number of responses used to estimate parameters for each item.

TotalTime

Total computation time (in seconds) for the estimation process.

See est_irt(), est_mg(), and est_item() for more details.

Value

The internal component extracted from an object of class est_irt, est_mg, or est_item, depending on the input to the x argument.

Methods (by class)

Author(s)

Hwanggyu Lim hglim83@gmail.com

See Also

est_irt(), est_mg(), est_item()

Examples


# Fit a 2PL model to the LSAT6 data
mod.2pl <- est_irt(data = LSAT6, D = 1, model = "2PLM", cats = 2)

# Extract item parameter estimates
(est.par <- getirt(mod.2pl, what = "par.est"))

# Extract 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"))



[Package irtQ version 1.0.0 Index]