# maxDissim

0th

Percentile

##### Maximum Dissimilarity Sampling

Functions to create a sub-sample by maximizing the dissimilarity between new samples and the existing subset.

##### Usage
maxDissim(a, b, n = 2, obj = minDiss, randomFrac = 1, verbose = FALSE, ...)
minDiss(u)
sumDiss(u)
##### Arguments
a
a matrix or data frame of samples to start
b
a matrix or data frame of samples to sample from
n
the size of the sub-sample
obj
an objective function to measure overall dissimilarity
randomFrac
a number in (0, 1] that can be used to sub-sample from the remaining candidate values
verbose
a logical; should each step be printed?
...
optional arguments to pass to dist
u
a vector of dissimilarities
##### Details

Given an initial set of m samples and a larger pool of n samples, this function iteratively adds points to the smaller set by finding with of the n samples is most dissimilar to the initial set. The argument obj measures the overall dissimilarity between the initial set and a candidate point. For example, maximizing the minimum or the sum of the m dissimilarities are two common approaches.

This algorithm tends to select points on the edge of the data mainstream and will reliably select outliers. To select more samples towards the interior of the data set, set randomFrac to be small (see the examples below).

##### Value

• a vector of integers corresponding to the rows of b that comprise the sub-sample.

##### References

Willett, P. (1999), "Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of Compounds," {Journal of Computational Biology, 6, 447-457.} [object Object]

dist example <- function(pct = 1, obj = minDiss, ...) { tmp <- matrix(rnorm(200 * 2), nrow = 200)

# start with 15 data points start <- sample(1:dim(tmp)[1], 15) base <- tmp[start,] pool <- tmp[-start,] # select 9 for addition newSamp <- maxDissim( base, pool, n = 9, randomFrac = pct, obj = obj, ...) allSamp <- c(start, newSamp) plot( tmp[-newSamp,], xlim = extendrange(tmp[,1]), ylim = extendrange(tmp[,2]), col = "darkgrey", xlab = "variable 1", ylab = "variable 2") points(base, pch = 16, cex = .7) for(i in seq(along = newSamp)) points( pool[newSamp[i],1], pool[newSamp[i],2], pch = paste(i), col = "darkred") }

par(mfrow=c(2,2))

set.seed(414) example(1, minDiss) title("No Random Sampling, Min Score")

set.seed(414) example(.1, minDiss) title("10 Pct Random Sampling, Min Score")

set.seed(414) example(1, sumDiss) title("No Random Sampling, Sum Score")

set.seed(414) example(.1, sumDiss) title("10 Pct Random Sampling, Sum Score")

utilities

##### Aliases
• maxDissim
• minDiss
• sumDiss
Documentation reproduced from package caret, version 3.32, License: GPL-2

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