Principal surface fits are now displayed as contour biplots. plot() draws
each variable as the contour lines of its fitted surface coordinate function
f_j, so a variable's value is read off its contours at a sample's
position on the surface, in place of the linear axes of a conventional biplot.
plot() gains arguments for tailoring the display: vars to select which
variables are drawn, group to colour samples by a factor, nlevels and
col_contour to control the contour axes, and main / outer_main to title
individual panels and the overall figure.
predictivity() reports sample predictivity — the proportion of each
sample's squared length that is reconstructed by the fitted surface. The
per-sample values are returned as a vector, with the mean over all samples in
the "overall" attribute.
contour_predictive_error() reports, for each variable, the root-mean-square
difference between the value read from its contour lines and the sample's
actual value. This measures how accurately a variable can be recovered by
reading its contours; the mean over variables is in the "overall" attribute.
predict() returns the values read from the contour axes at each sample's
position, back-transformed to the variables' original scales.
fitted() returns the fitted surface coordinates f_j(lambda_i)
for every sample and variable, in the working (centred / scaled) units.
print() summarises a fit: the number of samples and variables, the loess
span, the number of iterations to convergence, and the variable names.
The package is now released under the MIT licence (previously GPL-3).
Added a package website at https://raeesaganey91.github.io/prinsurf/ and a bug tracker at https://github.com/RaeesaGaney91/prinsurf/issues.
The Description field now cites Hastie and Stuetzle (1989)
doi:10.1080/01621459.1989.10478797.
Added the vignette Contour biplots, working through a fit and its diagnostics end to end.