fsPRA: Reduce Dimensions by Log-Ratio Selection

In tpq/exprso: Rapid Deployment of Machine Learning Algorithms

Description

fsPRA finds the most explanatory pairwise log-ratios using the variable selection method proposed by Michael Greenacre in "Variable Selection in Compositional Data Analysis Using Pairwise Logratios", modified to run faster.

Usage

fsPRA(object, top = 0, ...)

Arguments

object	An ExprsArray object to undergo feature selection.
top	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set top = 0 to include all features. A numeric vector can also be used to indicate specific features by location, similar to a character vector.
...	Arguments passed to the detailed function.

Value

Returns an ExprsArray object.

tpq/exprso documentation built on July 27, 2019, 8:44 a.m.