cog_irt: Fit Item Response Theory Models with Optional Contrast...

In cogirt: Cognitive Testing Using Item Response Theory

Fit Item Response Theory Models with Optional Contrast Effects

Description

This function estimates item response theory (IRT) model parameters. Users can optionally estimate person parameters that account for experimental or longitudinal contrast effects.

Usage

cog_irt(
  data = NULL,
  model = NULL,
  guessing = NULL,
  contrast_codes = NULL,
  num_conditions = NULL,
  num_contrasts = NULL,
  constraints = NULL,
  key = NULL,
  link = "probit",
  verbose = TRUE,
  ...
)

Arguments

data	A matrix of item responses (K by IJ). Rows should contain each subject's dichotomous responses (1 or 0) for the items indexed by each column.
model	An IRT model name. The options are "1p" for the one-parameter model, "2p" for the two parameter model, "3p" for the three-parameter model, or "sdt" for the signal detection-weighted model.
guessing	Either a single numeric guessing value or a matrix of item guessing parameters (IJ by 1). This argument is only used when model = '3p'.
contrast_codes	Either a matrix of contrast codes (JM by MN) or the name in quotes of a R stats contrast function (i.e., "contr.helmert", "contr.poly", "contr.sum", "contr.treatment", or "contr.SAS"). If using the R stats contrast function items in the data matrix must be arranged by condition.
num_conditions	The number of conditions (required if using the R stats contrast function or when constraints = TRUE).
num_contrasts	The number of contrasts including intercept (required if using the R stats contrast function or when constraints = TRUE).
constraints	Either a logical (TRUE or FALSE) indicating that item parameters should be constrained to be equal over the J conditions or a 1 by I vector of items that should be constrained to be equal across conditions.
key	An item key vector where 1 indicates target and 2 indicates distractor (IJ). Required when model = 'sdt'.
link	The name ("logit" or "probit") of the link function to be used in the model.
verbose	Logical (TRUE or FALSE) indicating whether to print progress.
...	Additional arguments.

Value

A list with elements for all parameters estimated (omega1, nu1, and/or lambda1), information values for all parameters estimated (info1_omega, info1_nu, and/or info1_lambda), the model log-likelihood value (log_lik), and the total number of estimated parameters (par) in the model.

Dimensions

I = Number of items per condition; J = Number of conditions or time points; K = Number of examinees; M Number of ability (or trait) dimensions; N Number of contrast effects (including intercept).

References

Embretson S. E., & Reise S. P. (2000). Item response theory for psychologists. Mahwah, N.J.: L. Erlbaum Associates.

Thomas, M. L., Brown, G. G., Patt, V. M., & Duffy, J. R. (2021). Latent variable modeling and adaptive testing for experimental cognitive psychopathology research. Educational and Psychological Measurement, 81(1), 155-181.

Examples

nback_fit_contr <- cog_irt(data = nback$y, model = "sdt",
                           contrast_codes = "contr.poly", key = nback$key,
                           num_conditions = length(unique(nback$condition)),
                           num_contrasts = 2)
plot(nback_fit_contr)

cogirt documentation built on April 3, 2025, 8:14 p.m.