# Cross-table of two fuzzy classifications

### Description

Calculates a cross-tabulated matrix relating two fuzzy membership matrices

### Usage

1 |

### 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 |

### 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.

### See Also

`defuzzify`

, `vegclust`

### Examples

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)
``` |