bpCent(pc, clsAsgn, data.pts = TRUE, centroids = TRUE, choices = 1:2, scale = 1, pc.biplot=FALSE, var.axes = TRUE, col, cex = rep(par("cex"), 2), xlabs = NULL, ylabs = NULL, expand=1, xlim = NULL, ylim = NULL, arrow.len = 0.1, main = NULL, sub = NULL, xlab = NULL, ylab = NULL, ...)
prcomp
object of the data used in clustering.TRUE
the point for each record is plotted.TRUE
the centroid for each cluster is plotted.lambda ^ scale
and the observations
are scaled by lambda ^ (1-scale)
, where lambda
are the eigen
values of the principal components solution. scale
should be between
0 and 1.lambda = 1
and the observations are are
scaled up the sqrt(n) and the variables scaled down by sqrt(n). In this case
the inner product between variables approximate covariances, and the
distances between observations approximate Mahalanobis distance. Gabriel
refers to this as a "principal component biplot".TRUE
the second set of points have arrows
representing them as (unscaled) axes.palette
: if there it and the next colour as used,
otherwise the first two colours of the paletter are used.x
, or
1:n
is the dimname is NULL
.y
, or
1:n
is the dimname is NULL
.var.axes
is true. The arrow head can be suppressed by
arrow.len = 0
.biplot
in order to allow the cluster
centroids of a clustering solution to be displayed as well as the individual
data points. If both data.pts
and centroids
are set to
FALSE
then only the variable directional vectors are displayed. Graphical parameters can also be given to biplot
.
biplot