edina {edina} | R Documentation |
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.
auto_edina()
,
summary.edina()
,
print.edina()
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)
}