vegan (version 1.6-0)

decostand: Standardizaton Methods for Community Ecology

Description

The function provides some popular (and effective) standardization methods for community ecologists.

Usage

decostand(x, method, MARGIN)
wisconsin(x)

Arguments

x

Community data matrix.

method

Standardization method.

MARGIN

Margin, if default is not acceptable.

Value

• Returns the standardized data frame.

Details

The function offers following standardization methods for community data:

• total: divide by margin total (defaultMARGIN = 1).
• max: divide by margin maximum (defaultMARGIN = 2).
• freq: divide by margin maximum and multiply by number of non-zero items, so that the average of non-zero entries is one (Oksanen 1983; defaultMARGIN = 2).
• normalize: make margin sum of squares equal to one (defaultMARGIN = 1).
• range: standardize values into range 0...1 (defaultMARGIN = 2).
• standardize: scale into zero mean and unit variance (defaultMARGIN = 2).
• pa: scale into presence/absence scale (0/1).
• chi.square: divide by row sums and square root of column sums, and adjust for square root of matrix total (Legendre & Gallagher 2001). When used with Euclidean distance, the matrix should be similar to the the Chi-square distance used in correspondence analysis. However, the results fromcmdscalewould still differ, since CA is a weighted ordination method (defaultMARGIN = 1).

Standardization, as contrasted to transformation, means that the entries are transformed relative to other entries.

All methods have a default margin. MARGIN=1 means rows (sites in a normal data set) and MARGIN=2 means columns (species in a normal data set).

Command wisconsin is a shortcut to common Wisconsin double standardization where species (MARGIN=2) are first standardized by maxima (max) and then sites (MARGIN=1) by site totals (tot).

References

Legendre, P. & Gallagher, E.D. (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129: 271--280.

Oksanen, J. (1983) Ordination of boreal heath-like vegetation with principal component analysis, correspondence analysis and multidimensional scaling. Vegetatio 52, 181--189.

Examples

data(varespec)
sptrans <- decostand(varespec, "max")
apply(sptrans, 2, max)
sptrans <- wisconsin(varespec)
# Chi-square: Similar but not identical to Correspondence Analysis.
sptrans <- decostand(varespec, "chi.square")
plot(procrustes(rda(sptrans), cca(varespec)))
# Hellinger transformation (Legendre & Callagher 2001):
sptrans <- sqrt(decostand(varespec, "total"))