dich_response_model: Dichotomous Response Model

In 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,
  omega = NULL,
  gamma = NULL,
  lambda = NULL,
  zeta = NULL,
  nu = NULL,
  kappa = NULL,
  link = NULL
)

Arguments

y	Item response matrix (K by IJ).
omega	Contrast effects matrix (K by MN).
gamma	Contrast codes matrix (JM by MN).
lambda	Item slope matrix (IJ by JM).
zeta	Specific effects matrix (K by JM).
nu	Item intercept matrix (IJ by 1).
kappa	Item guessing matrix (IJ by 1).
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.

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