calculate_overlap_coefficients {GeneSelectR}

Calculate Overlap and Similarity Coefficients between Feature Lists

Description

This function calculates the Overlap, Jaccard, and Soerensen-Dice coefficients to quantify the similarity between feature lists. In addition to feature importance and permutation importance, you can provide a custom list of feature names to be included in the overlap calculation.

Usage

calculate_overlap_coefficients(pipeline_results, custom_lists = NULL)

Arguments

pipeline_results

A PipelineResults object containing the fitted pipelines, cross-validation results, selected features, mean performance, and mean feature importances.

custom_lists

An optional named list of character vectors. Each character vector should contain feature names. The names of the list will be used as names in the resulting overlap coefficient matrices.

Value

A list containing lists of matrices, where each list corresponds to a different type of feature list (inbuilt feature importance, permutation importance, and custom lists if provided). Within each of these lists, there are three matrices showing the Overlap, Jaccard, and Soerensen-Dice coefficients for the feature lists: - @field overlap: A matrix showing the Overlap coefficients. - @field jaccard: A matrix showing the Jaccard coefficients. - @field soerensen: A matrix showing the Soerensen-Dice coefficients. These matrices compare the feature lists against each other, providing a numerical measure of their similarity. Note: If permutation importance data is not present in the pipeline_results, the corresponding list entry will be absent.

Examples

# Basic Usage with Mock Data
# Create a mock PipelineResults object with minimal data
mock_pipeline_results <- new("PipelineResults",
                             inbuilt_feature_importance = list(
                             "FeatureSet1" = data.frame(feature = c("feature1", "feature2")),
                             "FeatureSet2" = data.frame(feature = c("feature2", "feature3"))),
                             permutation_importance = list(
                             "FeatureSet1" = data.frame(feature = c("feature3", "feature4")),
                             "FeatureSet2" = data.frame(feature = c("feature1", "feature4"))))

# Calculate overlap coefficients without custom lists
overlap_results <- calculate_overlap_coefficients(mock_pipeline_results)


# Including Custom Lists
# Create custom feature lists
custom_feature_lists <- list("CustomList1" = c("feature5", "feature6"),
                             "CustomList2" = c("feature6", "feature7"))

# Calculate overlap coefficients with custom lists
overlap_results_custom <- calculate_overlap_coefficients(mock_pipeline_results,
                                                         custom_feature_lists)