aweSOMscreeplot: Screeplot of SOM superclasses

In aweSOM: Interactive Self-Organizing Maps

Screeplot of SOM superclasses

Description

The screeplot, helps deciding the optimal number of superclasses. Available for both PAM and hierarchical clustering.

Usage

aweSOMscreeplot(
  som,
  nclass = 2,
  method = c("hierarchical", "pam"),
  hmethod = c("complete", "ward.D2", "ward.D", "single", "average", "mcquitty", "median",
    "centroid")
)

Arguments

som	kohonen object, a SOM created by the kohonen::som function.
nclass	number of superclasses to be visualized in the screeplot. Default is 2.
method	Method used for clustering. Hierarchical clustering ("hierarchical") and Partitioning around medoids ("pam") can be used. Default is hierarchical clustering.
hmethod	For hierarchicical clustering, the clustering method, by default "complete". See the stats::hclust documentation for more details.

Value

No return value, called for side effects.

Examples

## Build training data
dat <- iris[, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")]
### Scale training data
dat <- scale(dat)
## Train SOM
### Initialization (PCA grid)
init <- somInit(dat, 4, 4)
ok.som <- kohonen::som(dat, grid = kohonen::somgrid(4, 4, 'hexagonal'),
                       rlen = 100, alpha = c(0.05, 0.01),
                       radius = c(2.65,-2.65),
                       init = init, dist.fcts = 'sumofsquares')
## Group cells into superclasses (PAM clustering)
superclust <- cluster::pam(ok.som$codes[[1]], 2)
superclasses <- superclust$clustering
aweSOMscreeplot(ok.som, method = 'hierarchical',
                hmethod = 'complete', nclass = 2)

aweSOM documentation built on Aug. 30, 2022, 5:05 p.m.