edina (version 0.1.1)

edina: EDINA Estimation Routine

Description

Performs the Exploratory Deterministic Input, Noise and Gate Model (EDINA) estimation on a given data set with a prespecified k value.

Usage

edina(data, k = 3, burnin = 10000, chain_length = 20000)

Arguments

data

Binary responses to assessments in matrix form with dimensions \(N \times J\).

k

Number of Attribute Levels as a positive integer.

burnin

Number of Observations to discard on the chain.

chain_length

Length of the MCMC chain

Value

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.

See Also

auto_edina(), summary.edina(), print.edina()

Examples

# NOT RUN {
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)
}

# }