Performs the Exploratory Deterministic Input, Noise and Gate Model (EDINA)
estimation on a given data set with a prespecified k value.
edina(data, k = 3, burnin = 10000, chain_length = 20000)An edina object that contains:
coefficients: Estimated coefficients of the model fit
loglike_summed: Summed log-likelihood
loglike_pmean: Mean of log-likelihood
pi_classes: Latent classes
avg_q: Estimated Averaged Q Matrix
est_q: Estimated Dichotomous Q Matrix
or_tested: Odds Ratio used in the model selection.
sample_or: Odds Ratio for the sample.
n: Number of Observations
j: Number of Items
k: Number of Traits
burnin: Amount of iterations to discard
chain_length: Amount of iterations to retain.
timing: Duration of the run
dataset_name: Name of the data set used in estimation.
if(requireNamespace("simcdm", quietly = TRUE)) {
# Set a seed for reproducibility
set.seed(1512)
# Setup data simulation parameters
N = 1 # Number of Examinees / Subjects
J = 10 # Number of Items
K = 2 # Number of Skills / Attributes
# Note:
# Sample size and attributes have been reduced to create a minimally
# viable example that can be run during CRAN's automatic check.
# Please make sure to have a larger sample size...
# Assign slipping and guessing values for each item
ss = gs = rep(.2, J)
# Simulate an identifiable Q matrix
Q = simcdm::sim_q_matrix(J, K)
# Simulate subject attributes
subject_alphas = simcdm::sim_subject_attributes(N, K)
# Simulate items under the DINA model
items_dina = simcdm::sim_dina_items(subject_alphas, Q, ss, gs)
# Compute the edina model
edina_model = edina(items_dina, k = K)
# Display results
edina_model
# Provide a summary overview
summary(edina_model)
}
#> The EDINA model for items_dina with K = 2
#>
#> The model fit is as follows:
#> k bic dic heuristic
#> 2 11.21918 11.91657 1
#>
#> The estimated coefficients for the EDINA model are:
#> Guessing SD(Guessing) Slipping SD(Slipping)
#> Item1 0.4410 0.2420 0.2463 0.1907
#> Item2 0.2484 0.1933 0.4155 0.2448
#> Item3 0.2499 0.1935 0.4148 0.2448
#> Item4 0.4388 0.2432 0.2495 0.1939
#> Item5 0.4365 0.2423 0.2493 0.1931
#> Item6 0.4391 0.2421 0.2506 0.1935
#> Item7 0.2481 0.1923 0.4142 0.2448
#> Item8 0.2519 0.1942 0.4141 0.2447
#> Item9 0.2442 0.1908 0.4183 0.2460
#> Item10 0.4367 0.2429 0.2501 0.1945
#>
#> The estimated Q matrix is:
#> Trait1 Trait2
#> Item01 0.6181 0.6344
#> Item02 0.6805 0.6600
#> Item03 0.6694 0.6759
#> Item04 0.6330 0.6232
#> Item05 0.6220 0.6274
#> Item06 0.6250 0.6306
#> Item07 0.6673 0.6696
#> Item08 0.6647 0.6809
#> Item09 0.6730 0.6667
#> Item10 0.6333 0.6208