hyperoverlap_detect: Overlap detection in n-dimensional space using support vector...

In hyperoverlap: Overlap Detection in n-Dimensional Space

Description

Given a matrix containing the ecological data (x) and labels (y) for two entities, a support vector machine is trained and the predicted label of each point is evaluated. If every point has been classified correctly, the entities can be separated and they do not overlap.

Usage

hyperoverlap_detect(x, y, kernel = "polynomial", kernel.degree = 3, cost = 500,
       stoppage.threshold = 0.4, verbose = TRUE, set = FALSE)

Arguments

x	A matrix or data.frame containing the variables of interest for both entities.
y	A vector of labels.
kernel	Character. Either "linear" or "polynomial" (default = "polynomial").
kernel.degree	Parameter needed for kernel = polynomial (default = 3).
cost	Specifies the SVM margin 'hardness'. Default value is 50, but can be increased for improved accuracy (although this increases runtimes and memory usage).
stoppage.threshold	Numeric. If the number of points misclassified using a linear hyperplane exceeds this proportion of the number of observations, non-linear separation is not attempted. Must be between 0 and 1 (default = 0.2).
verbose	Logical. If TRUE, prints diagnostic messages.
set	Logical. Is this function being called as part of hyperoverlap_set()? Should not need to be changed.

Details

Input data should be preprocessed so that all variables are comparable (e.g. same order of magnitude). Polynomial kernels allow curvilinear decision boundaries to be found between entities (see https://www.cs.cmu.edu/~ggordon/SVMs/new-svms-and-kernels.pdf). Smaller values of kernel.degree permit less complex decision boundaries; biological significance is likely to be lost at values > 5.

Value

A hyperoverlap-class object

Examples

data = iris[which(iris$Species!=("versicolor")),]
x = hyperoverlap_detect(data[,1:3],data$Species, kernel="linear")

hyperoverlap documentation built on Aug. 10, 2021, 9:07 a.m.