## S3 method for class 'default':
bioenv(comm, env, method = "spearman", index = "bray",
upto = ncol(env), trace = FALSE, partial = NULL, ...)
## S3 method for class 'formula':
bioenv(formula, data, ...)cor.comm)
in vegdist. This is ignored if comm are dissimilarities.formula and data.env.cor.bioenv with a
summary method.vegdist. Then it selects all possible subsets of
environmental variables, scales the variables, and
calculates Euclidean distances for this subset using
dist. Then it finds the correlation between community
dissimilarities and environmental distances, and for each size of
subsets, saves the best result.
There are $2^p-1$ subsets of $p$ variables, and an exhaustive
search may take a very, very, very long time (parameter upto offers a
partial relief). The function can be called with a model formula where
the LHS is the data matrix and RHS lists the environmental variables.
The formula interface is practical in selecting or transforming
environmental variables.
With argument partial you can perform dist. The
partial item can be used with any correlation method,
but it is strictly correct only for Pearson.
Clarke & Ainsworth (1993) suggested this method to be used for
selecting the best subset of environmental variables in interpreting
results of nonmetric multidimensional scaling (NMDS). They recommended a
parallel display of NMDS of community dissimilarities and NMDS of
Euclidean distances from the best subset of scaled environmental
variables. They warned against the use of Procrustes analysis, but
to me this looks like a good way of comparing these two ordinations.
Clarke & Ainsworth wrote a computer program BIO-ENV giving the name to the current function. Presumably BIO-ENV was later incorporated in Clarke's PRIMER software (available for Windows). In addition, Clarke & Ainsworth suggested a novel method of rank correlation which is not available in the current function.
vegdist, dist, cor
for underlying routines, monoMDS and
metaMDS for ordination, procrustes for
Procrustes analysis, protest for an alternative, and
rankindex for studying alternatives to the default
Bray-Curtis index.# The method is very slow for large number of possible subsets.
# Therefore only 6 variables in this example.
data(varespec)
data(varechem)
sol <- bioenv(wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al, varechem)
sol
summary(sol)Run the code above in your browser using DataLab