distshape() now computes by default the
dissimilarity of Sottile and Adelfio (2019, eq. 1): concordance of the
signs of the second derivatives and
product of the shape and distance components,
integrated with the trapezoidal rule on the actual grid p.
Version 0.3.x used the first derivative and the mean of the two
components on the grid (1:n)/n; use
legacy = TRUE (or deriv = 1,
combine = "mean") to reproduce it.
clustEff(): the number of clusters is selected by
default with the largest gap between the heights of the dendrogram
(criterion = "height"); the criteria of the paper
("pi.out", "dist.rel") and the silhouette
(default of 0.3.x, used with legacy = TRUE) are options.
The default alpha is 0.25 with confidence bands (effects
curves) and 0.5 otherwise, as suggested in the paper.
‘fda’, ‘ggplot2’ and ‘ggpubr’ are no longer used: the functional
PCA of fpcac() is computed internally on a B-spline basis
(fpca_bspline()), graphics use base R; ‘qrcm’ is suggested.
library(clustEff) no longer attaches ‘qrcm’, ‘cluster’,
‘fda’, ‘ggpubr’ and ‘ggplot2’.
Removed (with an informative error, see
?"clustEff-defunct"):
| Removed in 1.0.0 | Use instead |
|---|---|
extract.object(Y, X, ...) |
fit the models with qrcm::iqr(), then
extract_curves(model) or
extract_curves(list_of_models) |
clustEff(Beta, Beta.lower, Beta.upper, k, ...) |
clustEff(curves, lower, upper, p, k, ...): the first
three positional arguments are unchanged, but the fourth is now the grid
p (pass k by name) |
diss.mat, cut.method,
approx.spline, nbasis, trace,
ask, plot |
diss, criterion,
smooth/df, verbose, a fixed
k, plot() |
fpcac(X, K, fd, niter, Ksteps, seed, disp) |
fpcac(curves, t, K, nstart, iter.max) (the second
argument is now t), set.seed(),
verbose |
opt.fpcac() correlation distance between scores
(default) |
Euclidean distance between scores (diss to supply
another) |
opt.fpcac(..., method) |
opt.fpcac(..., criterion) |
clustEff() accepts a matrix of curves, the output of
extract_curves(), or directly models fitted with
qrcm::iqr(); the grid p can be supplied; for
effects curves the second derivatives are computed from the fitted
functions.extract_curves(): effects curves, pointwise bands (any
level) and second derivatives from one or more
qrcm::iqr() fits.smooth = TRUE smooths noisy curves with
smooth.spline() before computing the dissimilarity.summary(); new
plot() types ("clusters",
"dendrogram", "silhouette",
"dissimilarity", "criterion").fpcac(): vectorized trimmed k-means with k-means++
initialization (init = "random" as before), handling of
empty clusters; opt.fpcac() excludes the trimmed curves
from the criteria and has print() and plot()
methods.clustEff(): wrong check of Beta.upper;
errors with k[1] > 2; conf.level ignored
without bands (quantiles 0.1 and 0.9 were always used); graphical
parameters are no longer modified.fpcac(): failure when the objective function exceeded
1e6; wrong evaluation grid when an fd object was
supplied.fpcac.clustEff() is more stable and faster; changes in
plot.clustEff() and summary.clustEff().