RDINA {cdmTools} | R Documentation |
Restricted DINA model
Description
Estimation of the restricted deterministic input, noisy "and" gate model (R-DINA; Nájera et al., 2023). In addition to the non-compensatory (i.e., conjunctive) condensation rule of the DINA model, the compensatory (i.e., disjunctive) rule of the DINO model can be also applied (i.e., R-DINO model). The R-DINA/R-DINO model should be only considered for applications involving very small sample sizes (N < 100; Nájera et al., 2023), and model fit evaluation and comparison with competing models (e.g., DINA/DINO, G-DINA) is highly recommended.
Usage
RDINA(
dat,
Q,
gate = "AND",
att.prior = NULL,
est = "Brent",
EM.args = list(maxitr = 1000, conv.crit = 1e-04, init.phi = 0.2, verbose = TRUE),
tau.alpha = "MAP",
seed = NULL
)
Arguments
dat |
A N individuals x J items ( |
Q |
A J items x K attributes Q-matrix ( |
gate |
Either a conjunctive ( |
att.prior |
A 2^K attributes vector containing the prior distribution for each latent class. The sum of all elements does not have to be equal to 1, since the vector will be normalized. Default is |
est |
Use the Brent's method ( |
EM.args |
A list of arguments in case the EM algorithm is used to estimate the model:
|
tau.alpha |
Attribute profile estimator (either |
seed |
Random number generation seed (e.g., to solve ties in case they occur with MLE or MAP estimation). Default is |
Value
RDINA
returns an object of class RDINA
.
MLE
Estimated attribute profiles with the MLE estimator (
matrix
).MAP
Estimated attribute profiles with the MAP estimator (
matrix
).EAP
Estimated attribute profiles with the EAP estimator (
matrix
).phi
Phi parameter estimate (
numeric
).post.probs
A (
list
) containing the estimates of the posterior probability of each examinee in each latent class (pp
), marginal posterior probabilities of attribute mastery (mp
), and posterior probability of each latent class (lp
).likelihood
A (
list
) containing the likelihood of each examinee in each latent class (lik_il
) and the model log-likelihood (logLik
).test.fit
Relative model fit indices (
list
).class.accu
A (
list
) containing the classification accuracy estimates at the test-level (tau
), latent class-level (tau_l
), and attribute-level (tau_k
).specifications
Function call specifications (
list
).
Author(s)
Pablo Nájera, Universidad Pontificia Comillas
References
Ma, W., & de la Torre, J. (2020). GDINA: An R package for cognitive diagnosis modeling. Journal of Statistical Software, 93(14). https://doi.org/10.18637/jss.v093.i14
Nájera, P., Abad, F. J., Chiu, C.-Y., & Sorrel, M. A. (2023). The Restricted DINA model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments. Journal of Educational and Behavioral Statistics.
Examples
library(GDINA)
Q <- sim30GDINA$simQ # Q-matrix
K <- ncol(Q)
J <- nrow(Q)
set.seed(123)
GS <- data.frame(guessing = rep(0.2, J), slip = rep(0.2, J))
sim <- simGDINA(20, Q, GS, model = "DINA")
simdat <- sim$dat # Simulated data
simatt <- sim$attribute # Generating attributes
fit.RDINA <- RDINA(simdat, Q) # Apply the GNPC method
ClassRate(fit.RDINA$EAP, simatt) # Check classification accuracy