PULS {puls}

Partitioning Using Local Subregions (PULS)

Description

PULS function for functional data (only used when you know that the data shouldn't be converted into functional because it's already smooth, e.g. your data are step function)

Usage

PULS(
  toclust.fd,
  method = c("pam", "ward"),
  intervals = c(0, 1),
  spliton = NULL,
  distmethod = c("usc", "manual"),
  labels = toclust.fd$fdnames[2]$reps,
  nclusters = length(toclust.fd$fdnames[2]$reps),
  minbucket = 2,
  minsplit = 4
)

Arguments

toclust.fd

A functional data object (i.e., having class fd) created from fda package. See fda::fd().

method

The clustering method you want to run in each subregion. Can be chosen between pam and ward.

intervals

A data set (or matrix) with rows are intervals and columns are the beginning and ending indexes of of the interval.

spliton

Restrict the partitioning on a specific set of subregions.

distmethod

The method for calculating the distance matrix. Choose between "usc" and "manual". "usc" uses fda.usc::metric.lp() function while "manual" uses squared distance between functions. See Details.

labels

The name of entities.

nclusters

The number of clusters.

minbucket

The minimum number of data points in one cluster allowed.

minsplit

The minimum size of a cluster that can still be considered to be a split candidate.

Details

If choosing distmethod = "manual", the L2 distance between all pairs of functions y_i(t) and y_j(t) is given by:

d_R(y_i, y_j) = \sqrt{\int_{a_r}^{b_r} [y_i(t) - y_j(t)]^2 dt}.

Value

A PULS object. See PULS.object for details.

See Also

fda::is.fd()

Examples

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")
PULS4_pam