prinsurf 2.0
New features
Principal surface fits are now displayed as contour biplots.
plot()draws each variable as the contour lines of its fitted surface coordinate functionf_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:varsto select which variables are drawn,groupto colour samples by a factor,nlevelsandcol_contourto control the contour axes, andmain/outer_mainto 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 coordinatesf_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.
Other changes
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
Descriptionfield 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.