I am completely new to multivariate analyses and I need an advice how to get it applied to my data and which analyses to choose for which purpose.
My dataset is presence/absence (or relative abundance score) of 100 species on 5000 squares, and for each square I have 100 environmental variables (many of them strongly correlated).
Out of those environmental variables, how do I recognize which variables are similar with respect to species abundance? Getting distance matrix would be great. Which analysis to choose?
I was looking in R package
vegan and the function
vegdist() seems pretty close, but it's on the community data matrix - I need it for the environmental variables but with respect to the species abundance.
EDIT: I found my very amateurish way to do it, but I don't know if it's correct because I don't understand this properly, so I would be grateful if a) you could check this and b) tell me better way how to do it:
require(vegan) c1 <- cca(df.sp, df.env) # species and environment data frames cf <- coef(c1) # Now I will scale each dimension by the eigenvalues so that each dimension is # weighted by its importance: cf.scaled <- cf*matrix(eigenvals(c1, model = "constrained"), nrow = nrow(cf), ncol = ncol(cf), byrow = TRUE) # finally compute the distance matrix: di <- as.matrix(dist(cf.scaled))