This function computes the parameter estimates of the generic form of the models by using penalized JML estimation. It allows users to adjust the default settings of the estimation.
generic_model(X, init_par = c(), setting = c())The dataset that is used for estimation.
The name of each items in the dataset.
A vector of the highest response category as many as the number of items.
The log likelihood of the estimation.
Type of the model that is used.
A vector of the DIF parameters of each items on each groups.
A vector of the natural logarithm of discrimination parameters of each items.
A vector of the difficulty parameter of each items' categories (thresholds).
A vector of the ability parameters of each individuals.
Input dataset as matrix or data frame with ordinal responses (starting from 0); rows represent individuals, column represent items.
Initial values of the estimated parameters.
Parameter settings which are listed in autoRaschOptions().
In the discrimination parameters estimation, instead of estimating the discrimination parameters, we are estimating the natural logarithm of the parameters to avoid negative values, \(\alpha = exp(\gamma)\).