optpart (version 3.0-3)

optimclass: 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 stride

pval

the minimum probability for inclusion in the list of indicators

counts

the minimum number of clusters for inclusion in the list

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

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.

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

indval

Examples

# NOT RUN {
data(shoshveg)
dis.bc <- dsvdis(shoshveg,'bray')
opt.2.10 <- stride(2:20,dis.bc)
# }
# NOT RUN {
optimclass(shoshveg,opt.2.10)
# }