Hierarchical clustering

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, ...)

# S3 method for default
CLUST(x, ...)

# S3 method for 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)
#> 'norm=TRUE' is used and this may be troublesome. See ?efourier #Details
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