Functions to create a sub-sample by maximizing the dissimilarity between new samples and the existing subset.
maxDissim(a, b, n = 2, obj = minDiss, useNames = FALSE, randomFrac = 1, verbose = FALSE, ...) minDiss(u) sumDiss(u)
| 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 |
| useNames | a logical: should the function return the row names (as opposed ot the row index) |
| 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 |
a vector of integers or row names (depending on useNames)
corresponding to the rows of b that comprise the sub-sample.
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).
Willett, P. (1999), "Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of Compounds," Journal of Computational Biology, 6, 447-457.
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)#> Error in loadNamespace("proxy"): there is no package called ‘proxy’title("No Random Sampling, Min Score")#> Error in title("No Random Sampling, Min Score"): plot.new has not been called yet#> Error in loadNamespace("proxy"): there is no package called ‘proxy’title("10 Pct Random Sampling, Min Score")#> Error in title("10 Pct Random Sampling, Min Score"): plot.new has not been called yet#> Error in loadNamespace("proxy"): there is no package called ‘proxy’title("No Random Sampling, Sum Score")#> Error in title("No Random Sampling, Sum Score"): plot.new has not been called yet#> Error in loadNamespace("proxy"): there is no package called ‘proxy’title("10 Pct Random Sampling, Sum Score")#> Error in title("10 Pct Random Sampling, Sum Score"): plot.new has not been called yet