# Clustering with several number of clusters

### Description

Performs several runs of function 'vegclust' (or 'vegclustdist') on a community data matrix (or distance matrix) using different number of clusters

### Usage

1 2 3 4 | ```
hier.vegclust(x, hclust, cmin=2,cmax=20, min.size=NULL, verbose=TRUE, ...)
hier.vegclustdist(x, hclust, cmin=2,cmax=20, min.size=NULL, verbose=TRUE, ...)
random.vegclust(x, cmin=2, cmax=20, nstart=10, min.size=NULL, verbose=TRUE, ...)
random.vegclustdist(x, cmin=2, cmax=20, nstart=10, min.size=NULL, verbose=TRUE, ...)
``` |

### Arguments

`x` |
For |

`hclust` |
A hierarchical clustering represented in an object of type |

`cmin` |
Number of minimum mobile clusters. |

`cmax` |
Number of maximum mobile clusters. |

`nstart` |
A number indicating how many random trials should be performed for each number of groups |

`min.size` |
If |

`verbose` |
Flag used to print which number of clusters is currently running. |

`...` |
Additional parameters for function |

### Details

Function `hier.vegclust`

takes starting cluster configurations from cuts of a dendrogram given by object `hclust`

. Function `random.vegclust`

chooses random objects as cluster centroids and for each number of clusters performs `nstart`

trials. Functions `hier.vegclustdist`

and `random.vegclustdist`

are analogous to `hier.vegclust`

and `random.vegclust`

but accept distance matrices as input.

### Value

Returns an object of type 'mvegclust' (multiple vegclust), which contains a list vector with objects of type `vegclust`

.

### Author(s)

Miquel De Cáceres, Forest Science Center of Catalonia

### See Also

`vegclust`

, `vegclustdist`

, `vegclass`

, `defuzzify`

, `hclust`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## 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 noise clustering from hierarchical clustering at different number of clusters
wetland.hc = hclust(dist(wetland.chord),method="ward")
wetland.nc1 = hier.vegclust(wetland.chord, wetland.hc, cmin=2, cmax=5,
m = 1.2, dnoise=0.75, method="NC")
## Create noise clustering from random seeds at different levels
wetland.nc2 = random.vegclust(wetland.chord, cmin=2, cmax=5, nstart=10,
m = 1.2, dnoise=0.75, method="NC")
``` |