vegclust-package {vegclust}R Documentation

Fuzzy Clustering of Vegetation Data Functions for fuzzy and hard clustering of vegetation data

Description

A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>. This package contains functions used to perform fuzzy and hard clustering of vegetation data under different models.

Details

The DESCRIPTION file:

Package: vegclust
Type: Package
Title: Fuzzy Clustering of Vegetation Data
Version: 2.0.2
Date: 2022-08-24
Authors@R: c( person('Miquel', 'De Cáceres', role=c('aut','cre'), email='miquelcaceres@gmail.com'))
Description: A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.
Depends: R (>= 3.4.0)
Imports: vegan
License: GPL (>= 2)
URL: https://emf-creaf.github.io/vegclust/
LazyLoad: yes
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: utils, knitr
Author: Miquel De Cáceres [aut, cre]
Maintainer: Miquel De Cáceres <miquelcaceres@gmail.com>

Index of help topics:

CAP                     Cumulative abundance profile (CAP)
CAS                     Cumulative abundance surface (CAS)
as.memb                 Turns into membership matrix
as.vegclust             Turns into vegclust objects
clustcentroid           Cluster centers of a classification
clustconst              Constancy table of a classification
clustvar                Cluster variance
concordance             Concordance between two classifications
conformveg              Conform two community data tables
crossmemb               Cross-table of two fuzzy classifications
defuzzify               Defuzzifies a fuzzy partition
hcr                     Heterogeneity-constrained random resampling
                        (HCR)
hier.vegclust           Clustering with several number of clusters
incr.vegclust           Noise clustering with increasing number of
                        clusters
interclustdist          Calculates the distance between pairs of
                        cluster centroids
medreg                  Regeneration of Mediterranean vegetation data
                        set
plot.CAP                Draws cummulative abundance profiles
plot.CAS                Draws a cummulative abundance surface
plot.mvegclust          Plots clustering results
relate.levels           Relates two clustering level results.
stratifyvegdata         Reshapes community data from individual into
                        stratified form
treedata                Synthetic vegetation data set with tree data
vegclass                Classifies vegetation communities
vegclust                Vegetation clustering methods
vegclust-package        Fuzzy Clustering of Vegetation Data Functions
                        for fuzzy and hard clustering of vegetation
                        data
vegclust2kmeans         Reshapes as kmeans object
vegclustIndex           Compute fuzzy evaluation statistics
vegdiststruct           Structural and compositional dissimilarity
wetland                 Wetland vegetation data set

Author(s)

NA Maintainer: NA

References

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

De Cáceres, M., Legendre, P., & He, F. 2013. Dissimilarity measurements and the size structure of ecological communities (D. Faith, Ed.). Methods in Ecology and Evolution 4: 1167–1177.

Examples

## Loads data  
data(wetland)
  
## This equals the chord transformation 
wetland.chord = as.data.frame(sweep(as.matrix(wetland), 1, 
                              sqrt(rowSums(as.matrix(wetland)^2)), "/"))

## Create noise clustering with 3 clusters. Perform 10 starts from random seeds 
## and keep the best solution
wetland.nc = vegclust(wetland.chord, mobileCenters=3, m = 1.2, dnoise=0.75, 
                      method="NC", nstart=10)

[Package vegclust version 2.0.2 Index]