Fits an Extended NOminal Response Model (ENORM) using conditional maximum likelihood (CML) or a Gibbs sampler for Bayesian estimation.
fit_enorm(dataSrc, predicate = NULL, fixed_params = NULL,
method = c("CML", "Bayes"), nIterations = 500)Data source: a dexter project db handle or a data.frame with columns: person_id, item_id, item_score
An optional expression to subset data, if NULL all data is used
Optionally, a prms object from a previous analysis or a data.frame with columns: item_id, item_score (omitting 0 score category) and beta. To facilitate the user in entering parameter values, we assume the parameterisation used by OPLM; in short, beta's are thresholds between categories. At this moment, it is not possible to fix some but not all categories of an item.
If CML, the estimation method will be Conditional Maximum Likelihood; otherwise, a Gibbs sampler will be used to produce a sample from the posterior
Number of Gibbs samples when estimation method is Bayes. The maximum number of iterations when using CML.
An object of type prms. The prms object can be cast to a data.frame of item parameters
using function `coef` or used directly as input for other Dexter functions.
Maris, G., Bechger, T.M. and San-Martin, E. (2015) A Gibbs sampler for the (extended) marginal Rasch model. Psychometrika. 2015; 80(4): 859<U+2013>879.
functions that accept a prms object as input: ability, plausible_values,
plot.prms