auto_edina {edina}

Auto EDINA model selection routine

Description

Automatically select an appropriate K dimension for a Q matrix under the Exploratory Deterministic Input, Noise And gate (EDINA) Model.

Usage

auto_edina(data, k = 2:4, 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 auto_edina object that contains:

• edina_models: A list containing all estimated edina model objects.

• criterions: Information criterions calculated for each model

• k_checked: Varying k dimensions checked.

• j: Number of Items

See Also

autoplot.auto_edina(), best_model(), model_selection_graph(), parameter_evolution_graph()

Examples

if(requireNamespace("simcdm", quietly = TRUE)) {

# Set a seed for reproducibility
set.seed(1512)

# Setup data simulation parameters
N = 15   # 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)


# Requires at least 15 seconds of execution time.
# Three EDINA models will be fit with increasing number of attributes.
model_set_edina = auto_edina(items_dina, k = 2:4)

# Display results
model_set_edina

# Retrieve criterion table
table = summary(model_set_edina)

# Extract "best model"
best_model(model_set_edina)

}