R: findThreshold

findThreshold {clst}

R Documentation

findThreshold

Description

Identify a distance threshold predicting whether a pairwise distance represents a comparison between objects in the same class (within-group comparison) or different classes (between-group comparison) given a matrix providing distances between objects and the group membership of each object.

Usage


findThreshold(dmat, groups, distances, method = "mutinfo", prob = 0.5,
              na.rm = FALSE, keep.dists = TRUE, roundCuts = 2, minCuts =
              20, maxCuts = 300, targetCuts = 100, verbose = FALSE,
              depth = 1, ...)

partition(dmat, groups, include, verbose = FALSE)

Arguments

`dmat`	Square matrix of pairwise distances.
`groups`	Object coercible to a factor identifying group membership of objects corresponding to either edge of `dmat`.
`include`	vector (numeric or boolean) indicating which elements to retain in the output; comparisons including an excluded element will have a value of NA
`distances`	Optional output of `partition` provided in the place of `dmat` and `groups`
`method`	The method for calculating the threshold; only 'mutinfo' is currently implemented.
`prob`	Sets the upper and lower bounds of `D` as some quantile of the within class distances and between-class differences, respectively.
`na.rm`	If TRUE, excludes `NA` elements in `groups` and corresponding rows and columns in `dmat`. Ignored if `distances` is provided.
`keep.dists`	If TRUE, the output will contain the `distances` element (output of `partition`).
`roundCuts`	Number of digits to round cutoff values (see Details)
`minCuts`	Minimal length of vector of cutoffs (see Details).
`maxCuts`	Maximal length of vector of cutoffs (see Details)
`targetCuts`	Length of vector of cutoffs if conditions met by `minCuts` and `maxCuts` are not met (see Details).
`verbose`	Terminal output is produced if TRUE.
`depth`	Private argument used to track level of recursion.
`...`	Extra arguments are ignored.

Details

findThreshold is used internally in classify, but may also be used to calculate a starting value of $D$.

partition is used to transform a square (or lower triangular) distance matrix into a data.frame containing a column of distances ($vals) along with a factor ($comparison) defining each distance as a within- or between-group comparison. Columns $row and $col provide indices of corresponding rows and columns of dmat.

Value

In the case of findThreshold, output is a list with elements decsribed below. In the case of partition, output is the data.frame returned as the element named $distances in the output of findThreshold.

`D`	The distance threshold (distance cutoff corresponding to the PMMI).
`pmmi`	Value of the point of maximal mutual information (PMMI)
`interval`	A vector of length 2 indicating the upper and lower bounds over which values for the threshold are evaluated.
`breaks`	A `data.frame` with columns `x` and `y` providing candidiate breakpoints and corresponding mutual information values, respectively.
`distances`	If `keep.distances` is TRUE, a data.frame containing pairwise distances identified as within- or between classes.
`method`	Character corresponding to input argument `method`.
`params`	Additional input parameters.

Author(s)

Noah Hoffman

Examples

data(iris)
dmat <- as.matrix(dist(iris[,1:4], method="euclidean"))
groups <- iris$Species
thresh <- findThreshold(dmat, groups, type="mutinfo")
str(thresh)

[Package clst version 1.28.0 Index]