fsPropd

An <code>ExprsArray</code> object to undergo feature selection.

object

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 <code>top = 0</code> to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.

A numeric scalar. Specifies the number of top features that should get
returned by the feature selection method. Use of <code>keep</code> is generally not
recommended, but can speed up analyses of large data.

keep

A logical scalar. Toggles whether to compute theta from
the feature ratios as provided. Set <code>modRatios = TRUE</code> if data were
recasted by a prior <code>modRatios</code> call. If <code>TRUE</code>, the <code>alpha</code>
and <code>weighted</code> arguments will not work.

modRatios

Arguments passed to the detailed function.

<code>fsPropd</code> selects features using the <code>propd</code> function
 from the <code>propr</code> package.

Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly building and deploying
supervised machine learning in a high-throughput manner. This package provides a
user-friendly interface that empowers investigators to execute state-of-the-art
binary and multi-class classification, as well as regression, with minimal
programming experience necessary.

fsPropd: Select Features by Differential Proportionality Analysis

Description

Usage

Arguments

Value