This function calculates predictions and log-likelihood values for a dichotomous response model framed using generalized latent variable modeling (GLVM; Skrondal & Rabe-Hesketh, 2004).
dich_response_model(
y = NULL,
omega = NULL,
gamma = NULL,
lambda = NULL,
zeta = NULL,
nu = NULL,
kappa = NULL,
link = NULL
)p = response probability matrix (K by IJ); yhatstar = latent response variate matrix (K by IJ); loglikelihood = model log-likelihood (scalar).
Item response matrix (K by IJ).
Contrast effects matrix (K by MN).
Contrast codes matrix (JM by MN).
Item slope matrix (IJ by JM).
Specific effects matrix (K by JM).
Item intercept matrix (IJ by 1).
Item guessing matrix (IJ by 1).
Choose between "logit" or "probit" link functions.
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).
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton: Chapman & Hall/CRC.