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)
| data | Binary responses to assessments in |
|---|---|
| k | Number of Attribute Levels as a positive |
| burnin | Number of Observations to discard on the chain. |
| chain_length | Length of the MCMC chain |
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