PearsonHeuristic: Feature-clustering based on Pearson Correlation Test.

In D2MCS: Data Driving Multiple Classifier System

Feature-clustering based on Pearson Correlation Test.

Description

Performs the feature-clustering using Pearson correlation tests. Valid for both, bi-class and multi-class problems.

Details

The test statistic is based on Pearson's product moment correlation coefficient cor(x, y) and follows a t distribution with length(x)-2 degrees of freedom if the samples follow independent normal distributions. If there are at least 4 complete pairs of observation, an asymptotic confidence interval is given based on Fisher's Z transform.

Super class

D2MCS::GenericHeuristic -> PearsonHeuristic

Methods

Public methods

• PearsonHeuristic$new()

• PearsonHeuristic$heuristic()

• PearsonHeuristic$clone()

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Method new()

Creates a PearsonHeuristic object.

Usage

PearsonHeuristic$new()

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Method heuristic()

Test for association between paired samples using Pearson test.

Usage

PearsonHeuristic$heuristic(col1, col2, column.names = NULL)

Arguments

col1

A numeric vector or matrix required to perform the clustering operation.

col2

A numeric vector or matrix to perform the clustering operation.

column.names

An optional character vector with the names of both columns.

Returns

A numeric vector of length 1 or NA if an error occurs.

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Method clone()

The objects of this class are cloneable with this method.

Usage

PearsonHeuristic$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dataset, cor

D2MCS documentation built on Aug. 23, 2022, 5:07 p.m.