plot.funkmeans: Plotting of k-means clustering results for massively parallel...
In vows: Voxelwise Semiparametrics

Description Usage Arguments Author(s) See Also Examples

Visualization of functional k-means clustering as implemented by funkmeans.

## S3 method for class 'funkmeans'
plot(x, fdobj = NULL, deriv = 0,
  ncluster = nrow(x$centers), new.array = TRUE, mfrow = NULL,
  colvec = NULL, cex.mtext = 0.7, xlabs = "", ylabs = "", titles = "",
  ...)

`x`	a functional k-means clustering object obtained from `funkmeans`.
`fdobj`	a functional data object, of class `"fd"`, defining the set of curves being clustered. By default, this is taken to be `x$fdobj`; but if the latter is `NULL`, `fdobj` must be specified. See the two cases in the example.
`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 `TRUE`, plots will be displayed in an array whose dimensions are set by the `mfrow` argument.
`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 `0` or `1`, depending on `ncluster`.
`colvec`	a vector of colors for the clusters. By default, this is set to the first `ncluster` elements of `c("dodgerblue", "green", "red", "orange", "yellow", "orchid",` `"brown", "grey", "purple")`, if `ncluster <= 9`.
`cex.mtext`	magnification for mtext command to display the size of each cluster above the corresponding subfigure.
`xlabs, ylabs, titles`	?????`NULL` or a character vector of length 1 or `ncluster`, specifying titles (x axis, y axis, overall titles) for each cluster. If vector's length equals 1, each cluster plot has the same title. By default, it's `NULL`
`...`	arguments passed to `plot.`

Yin-Hsiu Chen enjoychen0701@gmail.com, Philip Reiss phil.reiss@nyumc.org, Lan Huo, and Ruixin Tan

funkmeans

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)