clustergramInd {EcotoneFinder}

Clustergram with fuzzy indices plot

Description

Clustergram with fuzzy indices plot

Usage

clustergramInd(Data, k.range = 2:10,
  clustering.function = clustergram.kmeans,
  clustergram.plot = clustergram.plot.matlines,
  FuzzyIndice.plot = FuzzyIndice.plot.matlines, line.width = 0.004,
  add.center.points = TRUE, ...)

Arguments

Data

Should be a scales matrix. Where each column belongs to a different dimension of the observations.

k.range

A vector with the number of clusters to plot the clustergram for.

clustering.function

Which clustering method to be used. Default is k-means. Can be FCM is set to clustergram.vegclust. See details

clustergram.plot

Type of plot for the clustergram output. See details.

FuzzyIndice.plot

Type of plot for the fuzzy indices output. See details.

line.width

Graphical parameter. Width of the lines.

add.center.points

Logical. Should the cluster means be plotted (as points).

...

Additional arguments to be passed to the clustering function.

Details

This clustergram fuction produces an additional plot with the evolution of the main fuzzy indices (normalized partition coefficient (PCN) and normalized partition entropy (PEN)). Maximum values of PCN or minimum values of PEN can be used as criteria to choose the number of clusters.

Value

A clustergram plot and a fuzzy indices evolution plot of the inputed data

Examples

####### Example data:
   SyntheticTrial <- SyntheticData(SpeciesNum = 100,
                                   CommunityNum = 3, SpCo = NULL,
                                   Length = 500,
                                   Parameters = list(a=c(40, 80, 50),
                                                     b=c(100,250,400),
                                                     c=rep(0.03,3)),
                                   dev.c = .015, pal = c("#008585", "#FBF2C4", "#C7522B"))

   ######## clustergram plots with fuzzy indices plots:
   clustergramInd(as.matrix(SyntheticTrial[,2:ncol(SyntheticTrial)]),
                                 clustering.function = clustergram.vegclust.Ind,
                                 clustergram.plot = clustergram.plot.matlines,
                                 FuzzyIndice.plot = FuzzyIndice.plot.matlines,
                                 k.range = 2:10, line.width = .2)