Vector fitting
Fits ancillary variables to an ordination configuration.
vf(ord, vars, nperm = 100)
ord |
matrix containing a 2-dimensional ordination result with axes as columns. |
vars |
matrix with ancillary variables as columns. |
nperm |
number of permutation for the significance test. If nperm = 0, the test will be omitted. |
Vector fitting finds the maximum correlation of the individual variables with a configuration of samples in ordination space.
an object of class vf containing matrix with the first 2 columns containing the scores for every variable in each of the 2 dimensions of the ordination space. r is the maximum correlation of the variable with the ordination space, and pval is the result of the permutation test.
Sarah Goslee
Jongman, R.H.G., C.J.F. ter Braak and O.F.R. van Tongeren. 1995. Data analysis in community and landscape ecology. Cambridge University Press, New York.
# Example of multivariate analysis using built-in iris dataset data(iris) iris.d <- dist(iris[,1:4]) ### nmds() is timeconsuming, so this was generated ### in advance and saved. ### set.seed(1234) ### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4) ### save(iris.nmds, file="ecodist/data/iris.nmds.rda") data(iris.nmds) # examine fit by number of dimensions plot(iris.nmds) # choose the best two-dimensional solution to work with iris.nmin <- min(iris.nmds, dims=2) # fit the data to the ordination as vectors ### vf() is timeconsuming, so this was generated ### in advance and saved. ### set.seed(1234) ### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000) ### save(iris.vf, file="ecodist/data/iris.vf.rda") data(iris.vf) plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS") plot(iris.vf)
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