dich_response_model: Dichotomous Response Model

In mlthom/CogIRT: Cognitive Testing Using Item Response Theory

Dichotomous Response Model

Description

This function calculates predictions and log-likelihood values for a dichotomous response model framed using generalized latent variable modeling (GLVM; Skrondal & Rabe-Hesketh, 2004).

Usage

dich_response_model(
  y = NULL,
  nu = NULL,
  lambda = NULL,
  kappa = NULL,
  gamma = NULL,
  omega = NULL,
  zeta = NULL,
  link = "probit"
)

Arguments

y	Matrix of item responses (K by IJ).
nu	Matrix of item intercept parameters (K by IJ).
lambda	Matrix of item structure parameters (IJ by JM).
kappa	Matrix of item guessing parameters (K by IJ).
gamma	Matrix of experimental structure parameters (JM by MN).
omega	Examinee-level effects of the experimental manipulation (K by MN).
zeta	Condition-level effects of the experimental manipulation (K by JM).
link	Choose between logit or probit link functions.

Value

p = response probability matrix (K by IJ); yhatstar = latent response variate matrix (K by IJ); loglikelihood = model log-likelihood (scalar).

Dimensions

I = Number of items per condition; J = Number of conditions; K = Number of examinees; M Number of ability (or trait) dimensions; N Number of contrasts (should include intercept).

References

Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton: Chapman & Hall/CRC.

Examples

mod <- dich_response_model(y = sdirt$y, nu = sdirt$nu, lambda = sdirt$lambda,
                     gamma = sdirt$gamma, omega = sdirt$omega,
                     zeta = sdirt$zeta)

mlthom/CogIRT documentation built on June 13, 2022, 7:45 a.m.