| optimclass {optpart} | R Documentation |
Optimum Classification by Counts of Indicator Species
Description
Calculates the number of indicator species/cluster across a range of partitions
Usage
optimclass(comm, stride, pval = 0.01, counts = 2)
Arguments
comm |
a community matrix with sample units as rows and species as columns |
stride |
an object of class ‘stride’from function
|
pval |
the minimum probability for inclusion in the list of indicators |
counts |
the minimum number of clusters for inclusion in the list |
Details
Calculates the number of indicator species/cluster and the number of
clusters with at least ‘counts’ indicators, using the \phi index to
identify indicators with probabilities less than or equal to ‘pval’.
Arguably the optimal partition is the one with the most indicator species and
the most clusters with adequate indicators.
Value
A data.frame of
clusters |
number of clusters |
sig.spc |
the number of species with significant indicator value |
sig.clust |
the number of clusters with at least ‘counts’ indicator species |
Note
The concept and first implementation were by Tichy in software package ‘Juice’, and this is a simple port of the algorithm to R.
Author(s)
Lubomir Tichy wrote the original algorithm
David W. Roberts droberts@montana.edu
References
Tichy, L., M. Chytry, M. Hajek, S. Talbot, and Z. Botta-Dukat. 2010. OptimClass: Using species-to-cluster fidelity to determine the optimal partition in classification of ecological communities. J. Veg. Sci. 21:287-299.
See Also
Examples
data(shoshveg)
dis.bc <- dsvdis(shoshveg,'bray')
opt.2.10 <- stride(2:20,dis.bc)
## Not run: optimclass(shoshveg,opt.2.10)