GridSearch: Grid Search for Optimal Parameters

In exametrika: Test Theory Analysis and Biclustering

Grid Search for Optimal Parameters

Description

Performs a grid search to find optimal parameters for different analysis methods. Supports Biclustering, LCA (Latent Class Analysis), and LRA (Latent Rank Analysis).

Usage

GridSearch(
  obj,
  max_ncls = 10,
  max_nfld = 10,
  fun = "Biclustering",
  index = "BIC",
  ...
)

Arguments

obj	Input data matrix or object to be analyzed
max_ncls	Maximum number of classes/clusters to test (default: 10)
max_nfld	Maximum number of fields to test for Biclustering (default: 10)
fun	Function name to use for analysis. Options: "Biclustering", "LCA", "LRA" (default: "Biclustering")
index	Fit index to optimize from TestFitIndices returned by each function. Options: "AIC", "BIC", etc. (default: "BIC")
...	Additional arguments passed to the analysis function

Value

A list containing: For Biclustering:

index_matrix	Matrix of fit indices for each ncls/nfld combination
optimal_ncls	Optimal number of classes/clusters
optimal_nfld	Optimal number of fields
optimal_result	Analysis result using optimal parameters

For LCA/LRA:

index_vec	Vector of fit indices for each ncls
optimal_ncls	Optimal number of classes/clusters
optimal_result	Analysis result using optimal parameters

Examples

## Not run: 
# Grid search for Biclustering
result <- grid_serch(data_matrix, max_ncls = 5, max_nfld = 5)

# Grid search for LCA
result <- grid_serch(data_matrix, max_ncls = 8, fun = "LCA")

## End(Not run)

exametrika documentation built on Aug. 21, 2025, 5:27 p.m.