rank.multiDA: explore ranked features from a trained multiDA object

In sarahromanes/multiDA: High Dimensional Discriminant Analysis using Multiple Hypothesis Testing

Description

explore ranked features from a trained multiDA object

Usage

rank.multiDA(x, G)

Arguments

x	trained multiDA object
G	return rankings and features based on the number of groupings (G) per feature to be considered.
...	Any other variables which will be ignored

Value

dRank, a dataframe of summarised features, from which feature names can easily be fed into the plot.multiDA function for easy visualisation of the groupings

sarahromanes/multiDA documentation built on July 14, 2019, 9:41 p.m.