hyperoverlap_set: Pairwise overlap detection in n-dimensional space of multiple...
In hyperoverlap: Overlap Detection in n-Dimensional Space

Description Usage Arguments Details Value Examples

View source: R/hyperoverlap_set.r

This function is a wrapper for hyperoverlap_detect for pairwise overlap detection between multiple entities.

hyperoverlap_set(x, y, kernel = "polynomial",kernel.degree = 3, cost = 1000,
stoppage.threshold = 0.2, write.to.file = FALSE,
path = NULL,
sample.dimensionality.omit = "FALSE")

`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 1000, 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).
`write.to.file`	Logical. If TRUE, each `hyperoverlap-class` object is saved as a .rds file.
`path`	Character. Path to write .rds files to. Ignored if `write.to.file=FALSE`
`sample.dimensionality.omit`	Logical. If TRUE, omits any entity pairs with a combined sample size less than n+1, where n is the number of dimensions (see details).

In n dimensions, any set of points up to n+1 points can be separated using a linear hyperplane. This may produce an artefactual non-overlap result. The sample.dimensionality.omit parameter gives two options for dealing with these pairs when they form part of a larger analysis. If sample.dimensionality.omit = "TRUE", this pair is removed from the analysis (result = NA). If sample.dimensionality.omit = "FALSE", the pair is included, but a warning is printed.

A long-form matrix with the following columns: entity1, entity2, shape, polynomial.order (if kernel="polynomial"), result, number.of.points.misclassified.

If specified, individual Hyperoverlap-class objects are written to file.