decostand(x, method, MARGIN, range.global, logbase = 2, na.rm=FALSE, ...)wisconsin(x)
1 = rows,
and 2 = columns of x.method = "range". This allows using same ranges across
subsets of data. The dimensions of MARGIN must match with
x.method = "log"."decostand" giving the name of applied standardization
"method".total: divide by margin total (defaultMARGIN = 1).max: divide by margin maximum (defaultMARGIN = 2).freq: divide by margin maximum and multiply by the
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). If all values are constant, they will be
transformed to 0.standardize: scalexto zero mean and unit variance
(defaultMARGIN = 2).pa: scalexto 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 the Euclidean
distance, the distances should be similar to the
Chi-square distance used in correspondence analysis. However, the
results fromcmdscalewould still differ, since
CA is a weighted ordination method (defaultMARGIN = 1).hellinger: square root ofmethod = "total"(Legendre & Gallagher 2001).log: logarithmic transformation as suggested by
Anderson et al. (2006):$\log_b (x) + 1$for$x > 0$, where$b$is the base of the logarithm; zeros are
left as zeros. Higher bases give less weight to quantities and more
to presences, andlogbase = Infgives the presence/absence
scaling. Please note this isnot$\log(x+1)$.
Anderson et al. (2006) suggested this for their (strongly) modified
Gower distance (implemented asmethod = "altGower"invegdist), but the standardization can be used
independently of distance indices. 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).
Most standardization methods will give nonsense results with
negative data entries that normally should not occur in the community
data. If there are empty sites or species (or constant with
method = "range"), many standardization will change these into
NaN.
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: PCA similar but not identical to CA.
## Use wcmdscale for weighted analysis and identical results.
sptrans <- decostand(varespec, "chi.square")
plot(procrustes(rda(sptrans), cca(varespec)))Run the code above in your browser using DataLab