scaleDistPlot {clst} | R Documentation |
Produces annotated representations of two-dimensional
multidimensional scaling plots using cmdscale
.
scaleDistPlot(dmat, groups, fill, X, O, indices = "no", include, display, labels, shuffleGlyphs = NA, key = "top", keyCols = 4, glyphs, xflip = FALSE, yflip = FALSE, ...)
dmat |
Square matrix of pairwise distances. |
groups |
Object coercible to a factor identifying group
membership of objects corresponding to either edge of |
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 $text containing labels. |
shuffleGlyphs |
modify permutation of shapes and colors given an integer to serve as a random seed. |
key |
'right' (single column), 'top' (variable number of columns), or NULL for no key |
keyCols |
number of columns in key |
glyphs |
a data.frame with columns named |
xflip |
if TRUE, flip orientation of x-axis |
yflip |
if TRUE, flip orientation of y-axis |
... |
additional arguments are passed to |
Returns a lattice grid object.
Noah Hoffman
data(iris) dmat <- as.matrix(dist(iris[,1:4], method="euclidean")) groups <- iris$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))