dich_response_model {cogirt} | R Documentation |
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.