clustergram.vegclust.Ind {EcotoneFinder}

Vegclust clustering with fuzzy indices computation for clustergram

Description

Vegclust clustering with fuzzy indices computation for clustergram

Usage

clustergram.vegclust.Ind(Data, k, method = "FCM", ...)

Arguments

Data

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

k

Number of desired groups for the FCM clustering.

method

Clustering method for the vegclust function.

...

Additional parameters to be passed to the vegclust function.

Details

Additionally to the FCM clustering, the function compute the main fuzzy indices to help with the decision on the optimal number of cluster in the data. Maximum values of PCN or minimum values of PEN can be used as criteria to choose the number of clusters.

Value

A list containing the cluster vector, the centers matrix and a vector of four fuzzy indices (partition coefficient (PC), normalized partition coefficient (PCN), partition entropy (PE) and normalized partition entropy (PEN)). See vegclust and veclustIndex functions.

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)