Skip to contents

KMEANS on PCA objects

Source: R/mult-KMEANS.R
KMEANS.Rd

A very basic implementation of k-means. Beware that morphospaces are calculated so far for the 1st and 2nd component.

Usage

KMEANS(x, ...)

# S3 method for PCA
KMEANS(x, centers, nax = 1:2, pch = 20, cex = 0.5, ...)

Arguments

x

PCA object

...

additional arguments to be passed to kmeans

centers

numeric number of centers

nax

numeric the range of PC components to use (1:2 by default)

pch

to draw the points

cex

to draw the points

Value

the same thing as kmeans

See also

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

Examples

data(bot)
bp <- PCA(efourier(bot, 10))
#> 'norm=TRUE' is used and this may be troublesome. See ?efourier #Details
KMEANS(bp, 2)

#> K-means clustering with 2 clusters of sizes 26, 14
#> 
#> Cluster means:
#>           PC1          PC2
#> 1 -0.04036460  0.001738792
#> 2  0.07496282 -0.003229186
#> 
#> Clustering vector:
#>         brahma          caney         chimay         corona    deusventrue 
#>              1              1              2              1              1 
#>          duvel   franziskaner     grimbergen        guiness     hoegardeen 
#>              2              1              2              1              1 
#>        jupiler     kingfisher       latrappe lindemanskriek    nicechouffe 
#>              1              1              2              1              1 
#>     pecheresse   sierranevada     tanglefoot          tauro      westmalle 
#>              1              2              2              1              1 
#>          amrut    ballantines      bushmills         chivas        dalmore 
#>              1              2              1              2              2 
#>   famousgrouse    glendronach   glenmorangie   highlandpark    jackdaniels 
#>              1              1              1              2              1 
#>             jb  johnniewalker       magallan     makersmark           oban 
#>              1              1              1              2              1 
#>     oldpotrero      redbreast         tamdhu     wildturkey         yoichi 
#>              2              2              1              1              2 
#> 
#> Within cluster sum of squares by cluster:
#> [1] 0.02127484 0.03758606
#>  (between_SS / total_SS =  67.3 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
#> [6] "betweenss"    "size"         "iter"         "ifault"