Plot PULS Splitting Rule Tree

Print the PULS tree in the form of dendrogram.

Usage

# S3 method for class 'PULS'
plot(
  x,
  branch = 1,
  margin = c(0.12, 0.02, 0, 0.05),
  text = TRUE,
  which = 4,
  digits = getOption("digits") - 2,
  cols = NULL,
  col.type = c("l", "p", "b"),
  ...
)

Arguments

x

A PULS object.

branch

Controls the shape of the branches from parent to child node. Any number from 0 to 1 is allowed. A value of 1 gives square shouldered branches, a value of 0 give V shaped branches, with other values being intermediate.

margin

An extra fraction of white space to leave around the borders of the tree. (Long labels sometimes get cut off by the default computation).

text

Whether to print the labels on the tree.

which

Labeling modes, which are:

• 1: only splitting variable names are shown, no splitting rules.

• 2: only splitting rules to the left branches are shown.

• 3: only splitting rules to the right branches are shown.

• 4 (default): splitting rules are shown on both sides of branches.

digits

Number of significant digits to print.

cols

Whether to shown color bars at leaves or not. It helps matching this tree plot with other plots whose cluster membership were colored. It only works when text is TRUE. Either NULL, a vector of one color, or a vector of colors matching the number of leaves.

col.type

When cols is set, choose whether the color indicators are shown in a form of solid lines below the leaves ("l"), or big points ("p"), or both ("b").

...

Arguments to be passed to monoClust::plot.MonoClust().

Value

A plot of splitting order.

Examples

# \donttest{
library(fda)

# Build a simple fd object from already smoothed smoothed_arctic
data(smoothed_arctic)
NBASIS <- 300
NORDER <- 4
y <- t(as.matrix(smoothed_arctic[, -1]))
splinebasis <- create.bspline.basis(rangeval = c(1, 365),
                                    nbasis = NBASIS,
                                    norder = NORDER)
fdParobj <- fdPar(fdobj = splinebasis,
                  Lfdobj = 2,
                  # No need for any more smoothing
                  lambda = .000001)
yfd <- smooth.basis(argvals = 1:365, y = y, fdParobj = fdParobj)

Jan <- c(1, 31); Feb <- c(31, 59); Mar <- c(59, 90)
Apr <- c(90, 120); May <- c(120, 151); Jun <- c(151, 181)
Jul <- c(181, 212); Aug <- c(212, 243); Sep <- c(243, 273)
Oct <- c(273, 304); Nov <- c(304, 334); Dec <- c(334, 365)

intervals <-
  rbind(Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec)

PULS4_pam <- PULS(toclust.fd = yfd$fd, intervals = intervals,
                  nclusters = 4, method = "pam")
plot(PULS4_pam)

# }