caret (version 5.07-005)

maxDissim: Maximum Dissimilarity Sampling

Description

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, useNames = FALSE, 
         randomFrac = 1, verbose = FALSE, ...)
minDiss(u)
sumDiss(u)

Value

• a vector of integers or row names (depending on useNames) corresponding to the rows of b that comprise the sub-sample.

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).

References

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

See Also

dist

Examples

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")

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