fit_enorm: Fit the extended nominal response model
In dexter: Data Management and Analysis of Tests

fit_enorm

R Documentation

Fit the extended nominal response model

Description

Fits an Extended NOminal Response Model (ENORM) using conditional maximum likelihood (CML) or a Gibbs sampler for Bayesian estimation.

Usage

fit_enorm(
  dataSrc,
  predicate = NULL,
  fixed_params = NULL,
  method = c("CML", "Bayes"),
  nDraws = 1000,
  merge_within_persons = FALSE
)

Arguments

`dataSrc`	a connection to a dexter database, a matrix, or a data.frame with columns: person_id, item_id, item_score
`predicate`	An optional expression to subset data, if NULL all data is used
`fixed_params`	Optionally, a prms object from a previous analysis or a data.frame with parameters, see details.
`method`	If CML, the estimation method will be Conditional Maximum Likelihood; otherwise, a Gibbs sampler will be used to produce a sample from the posterior
`nDraws`	Number of Gibbs samples when estimation method is Bayes.
`merge_within_persons`	whether to merge different booklets administered to the same person, enabling linking over persons as well as booklets.

Details

To support some flexibility in fixing parameters, fixed_params can be a dexter prms object or a data.frame. If a data.frame, it should contain the columns item_id, item_score and a difficulty parameter. Three types of parameters are supported:

delta/beta: thresholds between subsequent item categories
eta: item-category parameters
b: exp(-eta)

Each type corresponds to a different parametrization of the model.

Value

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.

References

Maris, G., Bechger, T.M. and San-Martin, E. (2015) A Gibbs sampler for the (extended) marginal Rasch model. Psychometrika. 80(4), 859-879.

Koops, J. and Bechger, T.M. and Maris, G. (in press); Bayesian inference for multistage and other incomplete designs. In Research for Practical Issues and Solutions in Computerized Multistage Testing. Routledge, London.