# 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)
# rotate configuration so Sepal Width is along the horizontal axis
iris.nmin.rot <- rotate2d(iris.nmin, iris.vf[2, 1:2])
iris.vf.rot <- rotate2d(iris.vf, iris.vf[2, 1:2])
plot(iris.nmin.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf.rot)
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