nominal_to_int: Turn nominal (factor) data to integers, keep original levels...

In patriciamar/ShinyItemAnalysis: Test and Item Analysis via Shiny

Turn nominal (factor) data to integers, keep original levels with a key of correct responses alongside

Description

Convert a data.frame or tibble with factor variables (items) to integers, keeping the original factor levels (i.e. response categories) and correct answers (stored as an key attribute of each item) alongside.

Usage

nominal_to_int(Data, key)

Arguments

Data	data.frame or tibble with all columns being factors. Support for matrix is limited and behavior not guaranteed.
key	A single-column data.frame, (not matrix) tibble or - preferably - a factor vector of levels considered as correct responses.

Details

Fitting a nominal model using mirt::mirt() package requires the dataset to consist only of integers, arbitrarily representing the response categories. You can convert your dataset to integers on your own in that case.

On the other hand, BLIS model (and thus also the BLIRT parametrization) further requires the information of correct item response category. On top of that, the same information is leveraged when fitting a mirt model that conserves the "directionality" of estimated latent ability (using a model definition from obtain_nrm_def()). In these cases, you are recommended to use nominal_to_int() (note that fit_blis() and blis() does this internally). Note also that fitted BLIS model (of class BlisClass) stores the original levels with correct answer key in its orig_levels slot, accessible by a user via get_orig_levels().

Value

List of original levels with logical attribute key, which stores the information on which response (level) is considered correct. Note that levels not used in the original data are dropped.

See Also

Other BLIS/BLIRT related: BlisClass-class, coef,BlisClass-method, fit_blis(), get_orig_levels(), obtain_nrm_def(), print.blis_coefs()

patriciamar/ShinyItemAnalysis documentation built on April 25, 2024, 5:15 a.m.