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

### See Also

`plotDistances`, `plotMutinfo`

### 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 1.30.0 Index]