gpcm_dif: Estimation of The Generalized Partial Credit Model with DIF

Description

This function computes the parameter estimates of a generalized partial credit model with DIF for polytomous responses by using penalized JML estimation.

Usage

gpcm_dif(
  X,
  init_par = c(),
  groups_map = c(),
  setting = c(),
  method = c("fast", "novel")
)
# S3 method for gpcmdif
summary(object, ...)
# S3 method for gpcmdif
print(x, ...)

Value

X: The dataset that is used for estimation.
mt_vek: A vector of the highest responses given to items.
itemName: The vector of names of items (columns) in the dataset.
loglik: The log likelihood of the estimation.
hessian: The hessian matrix. Only when the isHessian = TRUE.
delta: A vector of the DIF parameters of each items on each groups.
gamma: A vector of the natural logarithm of discrimination parameters of each items.
beta: A vector of the difficulty parameter of each items' categories (thresholds).
theta: A vector of the ability parameters of each individuals.

Arguments

X: A matrix or data frame as an input with ordinal responses (starting from 0); rows represent individuals, columns represent items.
init_par: a vector of initial values of the estimated parameters.
groups_map: Binary matrix. Respondents membership to DIF groups; rows represent individuals, column represent group partitions.
setting: a list of the optimization control setting parameters.See autoRaschOptions()
method: The implementation option of log likelihood function. fast using a c++ implementation and novel using an R implementation.
object: The object of class 'gpcmdif'.
...: Further arguments to be passed.
x: The object of class 'gpcmdif'.

Details

In the discrimination parameters estimation, instead of estimating the discrimination parameters, we are estimating the natural logarithm of the parameters to avoid negative values, \(\alpha = exp(\gamma)\).

Examples

Run this code

if (FALSE) {
gpcmdif_res <- gpcm_dif(shortDIF, groups_map = c(rep(1,50),rep(0,50)))
summary(gpcmdif_res, par="delta")
}