decostand {vegan} | R Documentation |
The function provides some popular (and effective) standardization methods for community ecologists.
decostand(x, method, MARGIN) wisconsin(x)
x |
Community data matrix. |
method |
Standardization method. |
MARGIN |
Margin, if default is not acceptable. |
The function offers following standardization methods for community data:
total
: divide by margin total (default MARGIN = 1
).
max
: divide by margin maximum (default MARGIN = 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; default MARGIN = 2
).
normalize
: make margin sum of squares equal to one (default
MARGIN = 1
).
range
: standardize values into range 0 ... 1 (default
MARGIN = 2
).
standardize
: scale into zero mean and unit variance
(default MARGIN = 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 from cmdscale
would still differ, since
CA is a weighted ordination method (default MARGIN =
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
).
Returns the standardized data frame.
Common transformations can be made with standard R functions.
Jari Oksanen
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.
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"))