GVEM Algorithm for the Generalized Partial Credit Model

Usage

MGPCM_gvem(
  data,
  model = matrix(1, nrow = J, ncol = 4),
  group = rep(1, nrow(data)),
  iter = 2000,
  eps = 1e-05,
  SE = FALSE,
  verbose = TRUE,
  EFA = FALSE
)

Arguments

data: An $N\times J$ matrix of item responses where 0 is the minimal partial credit score (missing responses should be coded as NA)
model: A $J\times K$ matrix of loading indicators (K is the Number of latent dimension)(all items load on the only dimension by default)
iter: Maximum number of iterations
eps: Termination criterion on numerical accuracy
SE: Whether to calculate the standard errors
verbose: Whether to show the progress
EFA: Whether to rotate the output

Value

An object of class vemirt_DIF, which is a list containing the following elements:

...$Sigma: Group-level covariance matrices

...$MU: Person-level posterior mean vectors
...$a: Slopes for group 1
...$b: Intercepts for group 1
...$ll: Estimated lower bound of log-likelihood

Author

Yijun Cheng <chengxb@uw.edu>

Examples

if (FALSE) { # \dontrun{
with(MGPCM_data, MGPCM_gvem(data, model))} # }