Description Usage Arguments Author(s) See Also Examples
Visualization of functional k-means clustering as implemented by
funkmeans
.
1 2 3 4 5 |
x |
a functional k-means clustering object obtained from
|
fdobj |
a functional data object, of class |
deriv |
which derivative to display in the plots, which show 30 randomly selected curves, along with the cluster center, from each cluster. By default, the "0th derivative" is used (i.e., the curves themselves). |
ncluster |
number of clusters to display. By default, all are displayed. |
new.array |
logical: if |
mfrow |
a vector of length 2 giving the numbers of rows and columns for
the array of plots. By default, the number of rows will exceed the number of
columns by |
colvec |
a vector of colors for the clusters. By default, this is set
to the first |
cex.mtext |
magnification for mtext command to display the size of each cluster above the corresponding subfigure. |
xlabs, ylabs, titles |
????? |
... |
arguments passed to |
Yin-Hsiu Chen enjoychen0701@gmail.com, Philip Reiss phil.reiss@nyumc.org, Lan Huo, and Ruixin Tan
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(test)
d4 = test$d4
x = test$x
semi.obj = semipar4d(d4, formula = ~sf(x), data = data.frame(x = x), lsp=-5:5)
myfdobj = extract.fd(semi.obj)
# Case 1: fd object is stored in funkmeans object...
fkmobj = funkmeans(myfdobj, ncomp = 8, centers = 6)
plot(fkmobj)
# Case 2: fd object is not stored...
fkmobj = funkmeans(myfdobj, ncomp = 8, centers = 6, store.fdobj=FALSE)
plot(fkmobj, myfdobj)
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