R: Annotated multidimensional scaling plots.

scaleDistPlot {clst}

R Documentation

Annotated multidimensional scaling plots.

Description

Produces annotated representations of two-dimensional multidimensional scaling plots using cmdscale.

Usage


scaleDistPlot(dmat, groups, fill, X, O, indices = "no",
              include, display, labels,
              shuffleGlyphs = NA, key = "top",
              keyCols = 4, glyphs,
              xflip = FALSE, yflip = FALSE, ...)

Arguments

`dmat`	Square matrix of pairwise distances.
`groups`	Object coercible to a factor identifying group membership of objects corresponding to either edge of `dmat`.
`fill`	vector (logical or indices) of points to fill
`X`	vector of points to mark with an X
`O`	vector of points to mark with a circle
`indices`	label points with indices (all points if 'yes', or a subset indicated by a vector)
`include`	boolean or numeric vector of elements to include in call to cmdscale
`display`	boolean or numeric vector of elements to include in call to display
`labels`	list or data frame with parameters $i indicating indices and $text containing labels.
`shuffleGlyphs`	modify permutation of shapes and colors given an integer to serve as a random seed.
`key`	'right' (single column), 'top' (variable number of columns), or NULL for no key
`keyCols`	number of columns in key
`glyphs`	a data.frame with columns named `col` and `pch` corresponding to elements of `unique(groups)`
`xflip`	if TRUE, flip orientation of x-axis
`yflip`	if TRUE, flip orientation of y-axis
`...`	additional arguments are passed to `xyplot`

Value

Returns a lattice grid object.

Author(s)

Noah Hoffman

Examples

data(iris)
dmat <- as.matrix(dist(iris[,1:4], method="euclidean"))
groups <- iris$Species

## visualize pairwise euclidean dstances among items in the Iris data set
fig <- scaleDistPlot(dmat, groups)
plot(fig)

## leave-one-out analysis of the classifier
loo <- lapply(seq_along(groups), function(i){
  do.call(classify, pull(dmat, groups, i))
})
matches <- lapply(loo, function(x) rev(x)[[1]]$matches)
result <- sapply(matches, paste, collapse='-')
confusion <- sapply(matches, length) > 1
no_match <- sapply(matches, length) < 1
plot(scaleDistPlot(dmat, groups, fill=confusion, O=confusion, X=no_match))

[Package clst version 1.35.0 Index]