CLUST: Hierarchical clustering

In vbonhomme/Momocs: Morphometrics using R

Hierarchical clustering

Description

Performs hierarchical clustering through dist and hclust. So far it is mainly a wrapper around these two functions, plus plotting using the dendextend package facilities.

Usage

CLUST(x, ...)

## Default S3 method:
CLUST(x, ...)

## S3 method for class 'Coe'
CLUST(
  x,
  fac,
  type = c("horizontal", "vertical", "fan")[1],
  k,
  dist_method = "euclidean",
  hclust_method = "complete",
  retain = 0.99,
  labels,
  lwd = 1/4,
  cex = 1/2,
  palette = pal_qual,
  ...
)

Arguments

x	a Coe or PCA object
...	useless here
fac	factor specification for fac_dispatcher
type	character one of c("horizontal", "vertical", "fan") (default: horizontal)
k	numeric if provided and greater than 1, cut the tree into this number of groups
dist_method	to feed dist's method argument, that is one of euclidean (default), maximum, manhattan, canberra, binary or minkowski.
hclust_method	to feed hclust's method argument, one of ward.D, ward.D2, single, complete (default), average, mcquitty, median or centroid.
retain	number of axis to retain if a PCA object is passed. If a number < 1 is passed, then the number of PCs retained will be enough to capture this proportion of variance via scree_min
labels	factor specification for labelling tips and to feed fac_dispatcher
lwd	for branches (default: 0.25)
cex	for labels (default: 1)
palette	one of available palettes

Value

a ggplot plot

See Also

Other multivariate: KMEANS(), KMEDOIDS(), LDA(), MANOVA_PW(), MANOVA(), MDS(), MSHAPES(), NMDS(), PCA(), classification_metrics()

Examples

# On Coe
bf <- bot %>% efourier(6)
CLUST(bf)
# with a factor and vertical
CLUST(bf, ~type, "v")
# with some cutting and different dist/hclust methods
CLUST(bf,
      dist_method="maximum", hclust_method="average",
      labels=~type, k=3, lwd=1, cex=1, palette=pal_manual(c("green", "yellow", "red")))

# On PCA
bf %>% PCA %>% CLUST

vbonhomme/Momocs documentation built on Nov. 13, 2023, 8:54 p.m.