optindval: Optimizing Classification by Maximizing Dufrene and...

In optpart: Optimal Partitioning of Similarity Relations

Description

optindval is a iterative re-assignment classification algorithm that assigns samples to clusters to maximize the sum of indicator values.

Usage

optindval(comm,clustering,maxitr=100,minsiz=5)

Arguments

comm	a vegetation or other taxon table with samples as rows and taxa as columns
clustering	an index of cluster membership for each sample. May be either a numeric vector of length equal to the number of samples, or an object that inherits from class ‘cluster’
maxitr	the maximum number of iterations to attempt
minsiz	the minimum size cluster to consider reassigning a sample out of

Details

Iterative re-allocation algorithms temporarily re-assign each sample to each of the other possible clusters and calculate a goodness-of-clustering statistic for each re-assignment. The best of all possible re-assignments is then executed and the algorithm iterates until there are no more good re-assignments or the maximum number of iterations is reached. In optindval, the goodness-of-clustering statistic is the sum of Dufrene and Legendre indicator values

Value

a list of class "optindval","clustering" with components:

numitr	the number of iterations performed
sums	a vector of indicator value probability sums
clustering	the vector of cluster memberships (as integers) for each sample

Note

Like many iterative re-assignment algorithms, optindval is likely to be VERY slow from a random start or poor initial condition. optindval is maybe better used to polish existing classifications

Author(s)

David W. Roberts [e-mail]

References

http://ecology.msu.montana.edu/labdsv/R

See Also

optpart, opttdev, optsil

Examples

data(shoshveg) # returns a data.frame of vegetation data called shoshveg
dis.bc <- dsvdis(shoshveg,'bray') # generate Bray/Curtis dissimilarity 
                                  # matrix
opt.5 <- optpart(5,dis.bc) # generate 5-cluster optpart
## Not run: res <- optindval(shoshveg,opt.5) # polish the optpart result
## Not run: classmatch(opt.5,res) # see the plot re-assignments

Example output

Loading required package: cluster
Loading required package: labdsv
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
This is labdsv 2.0-1
convert existing ordinations with as.dsvord()

Attaching package: ‘labdsv’

The following object is masked from ‘package:stats’:

    density

Loading required package: MASS
Loading required package: plotrix

optpart documentation built on March 26, 2020, 6:18 p.m.