maxDists {clstutils} | R Documentation

## Select a maximally diverse set of items given a distance matrix.

### Description

Given a square matrix of pairwise distances, return indices of N objects with a maximal sum of pairwise distances.

### Usage

    maxDists(mat, idx = NA, N = 1,
             exclude = rep(FALSE, nrow(mat)),
             include.center = TRUE)

### Arguments

`mat` | square distance matrix
`idx` | starting indices; if missing, starts with the object with the maximum median distance to all other objects.
`N` | total number of selections; length of idx is subtracted.
`exclude` | boolean vector indicating elements to exclude from the calculation.
`include.center` | includes the "most central" element (ie, the one with the smallest median of pairwise distances to all other elements) if TRUE

### Value

A vector of indices corresponding to the margin of `mat`.

### Note

Note that it is important to evaluate if the candidate sequences contain outliers (for example, mislabeled sequences), because these will assuredly be included in a maximally diverse set of elements!

### Author(s)

Noah Hoffman

### See Also

`findOutliers`

### Examples

    library(ape)
    library(clstutils)
    data(seqs)
    data(seqdat)
    efaecium <- seqdat$tax_name == 'Enterococcus faecium'
    seqdat <- subset(seqdat, efaecium)
    seqs <- seqs[efaecium,]
    dmat <- ape::dist.dna(seqs, pairwise.deletion=TRUE, as.matrix=TRUE, model='raw')

    ## find a maximally diverse set without first identifying outliers
    picked <- maxDists(dmat, N=10)
    picked
    prettyTree(nj(dmat), groups=ifelse(1:nrow(dmat) %in% picked,'picked','not picked'))

    ## restrict selected elements to non-outliers
    outliers <- findOutliers(dmat, cutoff=0.015)
    picked <- maxDists(dmat, N=10, exclude=outliers)
    picked
    prettyTree(nj(dmat), groups=ifelse(1:nrow(dmat) %in% picked,'picked','not picked'),
    X = outliers)

[Package clstutils version 1.38.0 Index]