[R] transforming data prior to CCA

[SRuhl]

Hi everyone,
I´m a student and relatively new to R so apologies in advance if this
question seems stupid or obvious to you.
I have collected a dataset with about 60 species of diatoms (count data from
19 different sample sites) and environmental variables for each site
(salinity, pH, etc.). It´s all in the same dataset but distinct in R through
the functions below

diat <- diatom [, 1:60]  ##species
envir<- diatom [, 61:66] ##environmental variables

The long-term plan is to perform a canonical correspondence analysis (CCA in
the vegan package) on it but the data obviously has to conform to some
standarts first. Ideally, any two variables should be in a linear
relationship and multivariate normality should be given as well as
homoscedasticity (I haven´t tested for this one yet, that´ll be another
adventure).  Now my data - surprise - does not conform to a normal
distribution nor do the relationships seem linear so I need to transform it
(but which parts?). The usual log transformation doesn't change anything so
I found this one (the poisson generalized linear model)
glm(formula, family=poisson(link=log), data=envir)

again, it doesn´t work because I dont know what formula to put in.
Any kind of help would be greatly appreciated, I am so lost...
Thanks in advance,
SRuhl


On a side-note: the CCA runs on my data already but what good is that when
the data is not in the right format? It may look completely different when
the data fits all the requirements.





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