fp_select_k_features: A feature preprocessor (FP) that reduces data to the k most...

In emeyers/NDTr: The Neural Decoding Toolbox in R

Description

This feature preprocessor object applies an ANOVA to the training data to find the p-value of all features. It then either uses the top k features with the smallest p-values, or it removes the features with the smallest k p-values. Additionally, this function can be used to remove the top k p-values and then use only the following j next smallest p-values (for example, this can be useful if one is interesting in comparing the performance using the most selective 10 neurons to using the next 10 most selective neurons, etc.).

Usage

fp_select_k_features(num_site_to_use = NA, num_sites_to_exclude = NA)

Arguments

num_site_to_use	The number of features with the smallest p-values to use.
num_sites_to_exclude	The number of features with the smallest p-values that should be excluded.

See Also

Other feature_preprocessor: fp_zscore()

Examples

# This will cause the cross-validator use only the 50 most selective sites
fp <- fp_select_k_features(num_site_to_use = 50)

# This will cause the cross-validator to remove the 20 most selective sites
fp <- fp_select_k_features(num_sites_to_exclude = 20)

# This will cause the cross-validator to remove the 20 most selective sites
# and then use only the 50 most selective sites that remain after the 20 are
# eliminated
fp <- fp_select_k_features(50, 20)

emeyers/NDTr documentation built on Aug. 8, 2020, 3:41 p.m.