Fuzzy Clustering of Vegetation Data Functions for fuzzy and hard clustering of vegetation data
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 ressemblance on the basis of structure and composition [De Caceres et al. (2013): <doi:10.1111/2041-210X.12116>]; and (3) to perform community trajectory analysis [De Caceres et al. (2019): <doi:10.1002/ecm.1350>]. This package contains functions used to perform fuzzy and hard clustering of vegetation data under different models.
The DESCRIPTION file:
| Package: | vegclust |
| Type: | Package |
| Title: | Fuzzy Clustering of Vegetation Data |
| Version: | 1.7.7 |
| Date: | 2019-01-08 |
| 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 ressemblance on the basis of structure and composition [De Caceres et al. (2013): <doi:10.1111/2041-210X.12116>]; and (3) to perform community trajectory analysis [De Caceres et al. (2019): <doi:10.1002/ecm.1350>]. |
| Depends: | R (>= 3.4.0), Rcpp (>= 0.12.12) |
| Imports: | sp, vegan, Kendall, circular, MASS |
| LinkingTo: | Rcpp |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
| Encoding: | UTF-8 |
| NeedsCompilation: | yes |
| RoxygenNote: | 6.1.1 |
| Suggests: | knitr, rmarkdown, RColorBrewer, smacof |
| 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
avoca Avoca permanent plot dataset
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
trajectories Community trajectory analysis
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 setNA Maintainer: NA
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.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.