splitSample

An <code>ExprsArray</code> object to split.

object

Specifies the percent of the total number
of subjects to include in the training set.

percent.include

For <code>splitSample</code>: additional arguments passed
along to <code><a rd-options="" href="/link/sample?package=exprso&version=0.5.1" data-mini-rdoc="exprso::sample">sample</a></code>. For <code>splitStratify</code>: additional
arguments passed along to <code><a rd-options="" href="/link/cut?package=exprso&version=0.5.1" data-mini-rdoc="exprso::cut">cut</a></code>.

<code>splitSample</code> builds a training and validation set by randomly sampling
 the subjects found within the <code>ExprsArray</code> object. Note that this method
 is not truly random. Instead, <code>splitSample</code> iterates through the random sampling
 process until it settles on a solution such that both the training and validation set
 contain at least one subject for each class label. If this method finds no solution
 after 10 iterations, the function will post an error. Set <code>percent.include = 100</code>
 to skip random sampling and return a <code>NULL</code> validation set. Additional arguments
 (e.g., <code>replace = TRUE</code>) passed along to <code><a rd-options="" href="/link/sample?package=exprso&version=0.5.1" data-mini-rdoc="exprso::sample">sample</a></code>.

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.

Thomas Quinn

exprso

Rapid Deployment of Machine Learning Algorithms

Daniel Tylee

Samuel Lee

splitSample function

For <code>splitSample</code>: additional arguments passed
along to <code><a rd-options='' href='sample'>sample</a></code>. For <code>splitStratify</code>: additional
arguments passed along to <code><a rd-options='' href='cut'>cut</a></code>.

<code>splitSample</code> builds a training and validation set by randomly sampling
 the subjects found within the <code>ExprsArray</code> object. Note that this method
 is not truly random. Instead, <code>splitSample</code> iterates through the random sampling
 process until it settles on a solution such that both the training and validation set
 contain at least one subject for each class label. If this method finds no solution
 after 10 iterations, the function will post an error. Set <code>percent.include = 100</code>
 to skip random sampling and return a <code>NULL</code> validation set. Additional arguments
 (e.g., <code>replace = TRUE</code>) passed along to <code><a rd-options='' href='sample'>sample</a></code>.

splitSample: Split by Random Sampling

Description

Usage

Arguments

Value