T4cluster (version 0.1.2)

funhclust: Functional Hierarchical Clustering

Description

Given \(N\) curves \(\gamma_1 (t), \gamma_2 (t), \ldots, \gamma_N (t) : I \rightarrow \mathbf{R}\), perform hierarchical agglomerative clustering with fastcluster package's implementation of the algorithm. Dissimilarity for curves is measured by \(L_p\) metric.

Usage

funhclust(
  fdobj,
  p = 2,
  method = c("single", "complete", "average", "mcquitty", "ward.D", "ward.D2",
    "centroid", "median"),
  members = NULL
)

Arguments

fdobj

a 'fd' functional data object of \(N\) curves by the fda package.

p

an exponent in \(L_p\) formalism (default: 2).

method

agglomeration method to be used. This must be one of "single", "complete", "average", "mcquitty", "ward.D", "ward.D2", "centroid" or "median".

members

NULL or a vector whose length equals the number of observations. See hclust for details.

Value

an object of class hclust. See hclust for details.

References

ferreira_comparison_2009T4cluster

Examples

# NOT RUN {
# -------------------------------------------------------------
#                     two types of curves
#
# type 1 : sin(x) + perturbation; 20 OF THESE ON [0, 2*PI]
# type 2 : cos(x) + perturbation; 20 OF THESE ON [0, 2*PI]
# -------------------------------------------------------------
## PREPARE : USE 'fda' PACKAGE
#  Generate Raw Data
datx = seq(from=0, to=2*pi, length.out=100)
daty = array(0,c(100, 40))
for (i in 1:20){
  daty[,i]    = sin(datx) + rnorm(100, sd=0.1)
  daty[,i+20] = cos(datx) + rnorm(100, sd=0.1)
}
#  Wrap as 'fd' object
mybasis <- fda::create.bspline.basis(c(0,2*pi), nbasis=10)
myfdobj <- fda::smooth.basis(datx, daty, mybasis)$fd

## RUN THE ALGORITHM 
hcsingle = funhclust(myfdobj, method="single")

## VISUALIZE
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
matplot(datx, daty[,1:20],  type="l", main="Curves Type 1")
matplot(datx, daty[,21:40], type="l", main="Curves Type 2")
plot(hcsingle, main="hclust with 'single' linkage")
par(opar)


# }