PULS: Partitioning Using Local Subregions (PULS)
In vinhtantran/PULS: Partitioning Using Local Subregions

Description Usage Arguments Details Value See Also Examples

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)

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
)

`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.

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

A PULS object. See PULS.object for details.

fda::is.fd()

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