fsPrcomp

Specifies the <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.

Arguments passed to the detailed function.

<code>fsPrcomp</code> reduces dimensions using the <code>prcomp</code> function.
 The reduction model is saved and deployed automatically on any new
 data during model validation.

Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly implementing 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, including deep learning, with minimal programming
experience necessary.

fsPrcomp: Reduce Dimensions by PCA

Description

Usage

Arguments

Value