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Last data update: 2014.03.03

R: Select a maximally diverse set of items given a distance...

maxDists

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

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(clstutils)
Loading required package: clst
Loading required package: rjson
Loading required package: ape
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/clstutils/maxDists.Rd_%03d_medium.png", width=480, height=480)
> ### Name: maxDists
> ### Title: Select a maximally diverse set of items given a distance matrix.
> ### Aliases: maxDists
> ### Keywords: classif
> 
> ### ** 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
S000651464 S000591191 S001794517 S001418165 S000427507 S000516063 S002166218 
        24        111         98         89         13         18        107 
S001352453 S002166217 S001155771 
        85        106         48 
> 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
S000651464 S000531199 S001045699 S001352453 S000591317 S001156205 S001155770 
        24         19         39         85         21         49         47 
S001153624 S000871538 S001292637 
        45         28         83 
> prettyTree(nj(dmat), groups=ifelse(1:nrow(dmat) %in% picked,'picked','not picked'),
+ X = outliers)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>