Auto EDINA model selection routine

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

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

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)

# \donttest{
# 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)
# }
}
#> Starting the estimation procedure ... 
#> Working on k = 2 ... 
#> Time Elapsed: 1.488
#> Working on k = 3 ... 
#> Time Elapsed: 1.92
#> Working on k = 4 ... 
#> Time Elapsed: 2.569
#> The EDINA model for data with K = 2 
#> 
#> The model fit is as follows:
#>  k      bic      dic  heuristic
#>  2 318.8249 175.2029 0.02222222
#> 
#> The estimated coefficients for the EDINA model are:
#>        Guessing SD(Guessing) Slipping SD(Slipping)
#> Item1    0.3959       0.1772   0.2386       0.1447
#> Item2    0.2637       0.1467   0.4050       0.1821
#> Item3    0.1220       0.1070   0.3213       0.1670
#> Item4    0.1414       0.1186   0.1433       0.1234
#> Item5    0.3301       0.1516   0.2359       0.1393
#> Item6    0.2192       0.1304   0.3106       0.1648
#> Item7    0.2278       0.1109   0.4920       0.2021
#> Item8    0.2920       0.1590   0.3375       0.1729
#> Item9    0.4198       0.1449   0.3401       0.1458
#> Item10   0.2272       0.1398   0.2441       0.1438
#> 
#> The estimated Q matrix is:
#>        Trait1 Trait2
#> Item01 0.6418 0.5205
#> Item02 0.6194 0.5121
#> Item03 0.5409 0.6620
#> Item04 0.4881 0.6256
#> Item05 0.5454 0.6601
#> Item06 0.5941 0.6817
#> Item07 0.7514 0.7612
#> Item08 0.6271 0.5074
#> Item09 0.6640 0.6284
#> Item10 0.5025 0.6315

Arguments

Value

See also

Examples