Apparently, similar conclusions can be drawn from plotting linear discriminant analysis results, though I am not certain what the LDA plot presents, hence the question. The axis are the two first linear discriminants (LD1 99% and LD2 1% of trace). The coordinates of the red vectors are "Coefficients of linear discriminants" also described as "scaling" (lda.fit$scaling: a matrix which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical). "scaling" is calculated as diag(1/f1, , p)
and f1 is sqrt(diag(var(x - group.means[g, ])))
. Data can be projected onto the linear discriminants (using predict.lda) (code below, as demonstrated http://stackoverflow.com/a/17240647/742447https://stackoverflow.com/a/17240647/742447). The data and the predictor variables are plotted together so that which species are defined by an increase in which predictor variables can be seen (as is done for usual PCA biplots and the above PCA biplot).: