Performs several runs of function 'vegclust' on a community data matrix using an increasing number of clusters until some conditions are met.
1 2 3  incr.vegclust(x, method="NC", ini.fixed.centers = NULL,
min.size = 10, max.var=NULL, alpha = 0.5,
nstart=100, fix.previous = TRUE, dnoise=0.75, m=1.0,...)

x 
Community data table. A site (rows) by species (columns) matrix or data frame. 
method 
A clustering model. Current accepted models are of the noise clustering family:

ini.fixed.centers 
The coordinates of initial fixed cluster centers. These will be used as 
min.size 
The minimum size (cardinality) of clusters. If any of the current k clusters does not have enough members the algorithm will stop and return the solution with k1 clusters. 
max.var 
The maximum variance allowed for clusters (see function 
alpha 
Criterion to choose cluster seeds from the noise class. Specifically, an object is considered as cluster seed if the membership to the noise class is larger than 
nstart 
A number indicating how many random trials should be performed for number of groups. Each random trial uses the k1 cluster centers plus the coordinates of the current cluster seed as initial solution for 
fix.previous 
Flag used to indicate that the cluster centers found when determining k1 clusters are fixed when determining k clusters. 
m 
The fuzziness exponent. 
dnoise 
The distance to the noise cluster. 
... 
Additional parameters for function 
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.
Returns an object of class vegclust
; or NULL
if the initial cluster does not contain enough members.
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, 270293.
vegclust
,hier.vegclust
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## 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)), "/"))
## Call incremental noise clustering
wetland.nc = incr.vegclust(wetland.chord, method="NC", m = 1.2, dnoise=0.75,
min.size=5)
## Inspect cluster sizes
print(wetland.nc$size)

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