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

### See Also

`cmdscale`, `xyplot`

### 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 1.28.0 Index]