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. -- View this message in context: http://r.789695.n4.nabble.com/transforming-data-prior-to-CCA-tp4663454.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
