M_constrained_irt: M_constrained_irt
In IRTM: Theory-Driven Item Response Theory (IRT) Models

M_constrained_irt

R Documentation

M_constrained_irt

Description

This function allows you to run the IRT model.

Usage

M_constrained_irt(
  Y,
  d,
  M = NULL,
  theta_fix = NULL,
  which_fix = NULL,
  nburn = 1000,
  nsamp = 1000,
  thin = 10,
  learn_Sigma = TRUE,
  learn_Omega = FALSE,
  hyperparameters = list(),
  display_progress = TRUE
)

Arguments

`Y`	a N x K matrix of responses given by N respondents to K items. Can contain missing values.
`d`	an integer specifying the number of latent dimensions.
`M`	a list of K d x d matrices (default=NULL).
`theta_fix`	a matrix with d columns containing the values of the latent dimensions for respondents that have pre-specified latent factors.
`which_fix`	a vector containing the indices of the respondents for which latent factors have been fixed.
`nburn`	an integer specifying the number of burn-in MCMC iterations.
`nsamp`	an integer specifying the number of sampling MCMC iterations.
`thin`	an integer specifying the number of thinning MCMC samples.
`learn_Sigma`	a Boolean specifying whether a covariance matrix for the latent factors should be learned.
`learn_Omega`	a Boolean specifying whether a covariance matrix for the latent loadings should be learned.
`hyperparameters`	a list of hyperparameters for the model.
`display_progress`	a Boolean specifying whether a progress bar should be displayed.

Value

A list containing the following components:

`lambda`	An array of dimension (K x d x nsamp/thin) containing posterior samples of item discrimination parameters.
`b`	A matrix of dimension (K x nsamp/thin) containing posterior samples of item difficulty parameters.
`theta`	An array of dimension (N x d x nsamp/thin) containing posterior samples of respondent latent trait values.
`Sigma`	An array of dimension (d x d x nsamp/thin) containing posterior samples of the covariance matrix of latent traits (only if learn_Sigma=TRUE).
`Omega`	An array of dimension (d x d x nsamp/thin) containing posterior samples of the covariance matrix of item loadings (only if learn_Omega=TRUE).