Description Usage Arguments Details Value Author(s) References See Also Examples

Classifies vegetation communities into a previous fuzzy or hard classification.

1 | ```
vegclass(y, x)
``` |

`y` |
An object of class |

`x` |
Community data to be classified, in form of a site by species matrix (if the vegclust object is in |

This function uses the classification model specified in `y`

to classify the communities (rows) in `x`

. When vegclust is in `raw`

mode, the function calls first to `conformveg`

in order to cope with different sets of species. See the help of `as.vegclust`

to see an example of `vegclass`

with distance matrices.

Returns an object of type `vegclass`

with the following items:

`method` |
The clustering model used in |

`m` |
The fuzziness exponent in |

`dnoise` |
The distance to the noise cluster used for noise clustering (models NC, NCdd, HNC, HNCdd). This is set to |

`eta` |
The reference distance vector used for possibilistic clustering (models PCM and PCMdd). This is set to |

`memb` |
The fuzzy membership matrix. |

`dist2clusters` |
The matrix of object distances to cluster centers. |

Miquel De Cáceres, Forest Science Center of Catalonia.

Davé, R. N. and R. Krishnapuram (1997) Robust clustering methods: a unified view. IEEE Transactions on Fuzzy Systems 5, 270-293.

Bezdek, J. C. (1981) Pattern recognition with fuzzy objective functions. Plenum Press, New York.

Krishnapuram, R. and J. M. Keller. (1993) A possibilistic approach to clustering. IEEE transactions on fuzzy systems 1, 98-110.

De Cáceres, M., Font, X, Oliva, F. (2010) The management of numerical vegetation classifications with fuzzy clustering methods [Related software]. Journal of Vegetation Science 21 (6): 1138-1151.

`vegclust`

, `as.vegclust`

, `kmeans`

, `cmeans`

, `conformveg`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
## Loads data (38 columns and 33 species)
data(wetland)
dim(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)), "/"))
## Splits wetland data into two matrices of 30x27 and 11x22
wetland.30 = wetland.chord[1:30,]
wetland.30 = wetland.30[,colSums(wetland.30)>0]
dim(wetland.30)
wetland.11 = wetland.chord[31:41,]
wetland.11 = wetland.11[,colSums(wetland.11)>0]
dim(wetland.11)
## Create noise clustering with 3 clusters from the data set with 30 sites.
wetland.30.nc = vegclust(wetland.30, mobileCenters=3, m = 1.2, dnoise=0.75,
method="NC", nstart=10)
## Cardinality of fuzzy clusters (i.e., the number of objects belonging to)
wetland.30.nc$size
## Classifies the second set of sites according to the clustering of the first set
wetland.11.nc = vegclass(wetland.30.nc, wetland.11)
## Fuzzy membership matrix
wetland.11.nc$memb
## Obtains hard membership vector, with 'N' for objects that are unclassified
defuzzify(wetland.11.nc$memb)$cluster
``` |

vegclust documentation built on May 29, 2018, 9:04 a.m.

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