ex2: Simulated Data for a Signal Detection Weighted IRT Model
In cogirt: Cognitive Testing Using Item Response Theory

ex2	R Documentation

Simulated Data for a Signal Detection Weighted IRT Model

Data and parameters were simulated based on example 2 provided for the sim_dich_response.R function.

ex2

A list with the following elements:

y: Matrix of dichotomous responses.
ystar: Matrix of latent response variates.
omega: Subject-level effects of the experimental manipulation.
omega_mu: Vector of means for the subject-level effects of the experimental manipulation (1 by K * M).
omega_sigma2: Covariance matrix for the subject-level effects of the experimental manipulation (K * M by K * M).
gamma: Contrast codes matrix.
lambda: Matrix of item slope parameters.
lambda_mu: Vector of means for the item slope parameters (1 by JM).
lambda_sigma2: Covariance matrix for the item slope parameters (JM by JM).
nu: Mean of the item intercept parameters (scalar).
nu_mu: Mean of the item intercept parameters (scalar).
nu_sigma2: Variance of the item intercept parameters (scalar).
zeta: Condition-level prediction errors.
zeta_mu: Vector of means for the condition-level prediction errors (1 by J * M).
zeta_sigma2: Covariance matrix for the condition-level prediction errors (J * M by J * M).
kappa: Item guessing matrix (K by IJ).
condition: Condition vector indiciting distinct conditions or time points.
key: Item key vector where 1 indicates target and 2 indicates distractor (IJ)