CEP_1t: Compute classification error probabilities for attributes

In LTCDM: Latent Transition Cognitive Diagnosis Model with Covariates

Compute classification error probabilities for attributes

Description

Function to compute classification error probabilities (CEP) for attributes (for single time-point situations).Details can be found in Iaconangelo (2017).

Usage

CEP_1t(GDINA.obj)

Arguments

GDINA.obj

a G-DINA model object returned from GDINA R package.

Value

a list with elements

SL.k

the sample-level correction weights

PDL.k

the posterior distribution level correction weights

References

Uses of classification error probabilities in the three-step approach to estimating cognitive diagnosis models (Doctoral dissertation). https://rucore.libraries.rutgers.edu/rutgers-lib/55495/PDF/1/play/

Examples

if(requireNamespace("GDINA")){
library(GDINA)
dat <- sim10GDINA$simdat
Q <- matrix(c(1,0,0,
              0,1,0,
              0,0,1,
              1,0,1,
              0,1,1,
              1,1,0,
              0,0,1,
              1,0,0,
              1,1,1,
              1,0,1),byrow = TRUE,ncol = 3)
fit.object <- GDINA(dat = dat, Q = Q, model = "GDINA", att.dist = "independent",verbose = FALSE)
CEP <- CEP_1t(fit.object)
}

LTCDM documentation built on Aug. 21, 2025, 5:26 p.m.