# crossmemb: Cross-table of two fuzzy classifications In vegclust: Fuzzy Clustering of Vegetation Data

## Description

Calculates a cross-tabulated matrix relating two fuzzy membership matrices

## Usage

 `1` ```crossmemb(x, y, relativize = TRUE) ```

## Arguments

 `x` A site-by-group fuzzy membership matrix. Alternatively, an object of class 'vegclust' or 'vegclass'. `y` A site-by-group fuzzy membership matrix. Alternatively, an object of class 'vegclust' or 'vegclass'. `relativize` If `TRUE` expresses the cross-tabulated values as proportions of cluster size in `x`.

## Value

A cross-tabulated matrix comparing the two classifications. In general, each cell's value is the (fuzzy) number of objects that in `x` are assigned to the cluster corresponding to the row and in `y` are assigned to the cluster corresponding to the column. If `relativize=TRUE` then the values of each row are divided by the (fuzzy) size of the corresponding cluster in `x`.

## Author(s)

Miquel De Cáceres, Forest Science Center of Catalonia.

`defuzzify`, `vegclust`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```## Loads data data(wetland) ## This equals the chord transformation ## (see also \code{\link{decostand}} in package vegan) wetland.chord = as.data.frame(sweep(as.matrix(wetland), 1, sqrt(rowSums(as.matrix(wetland)^2)), "/")) ## Create clustering with 3 clusters. Perform 10 starts from random seeds ## and keep the best solution. Try both FCM and NC methods: wetland.fcm = vegclust(wetland.chord, mobileCenters=3, m = 1.2, method="FCM", nstart=10) wetland.nc = vegclust(wetland.chord, mobileCenters=3, m = 1.2, dnoise=0.75, method="NC", nstart=10) ## Compare the results crossmemb(wetland.fcm, wetland.nc, relativize=FALSE) ```