Dear all, I would like to do variance partitioning of community dissimilarity matrix (Y) using 4 explanatory tables: X1=environmental characteristics X2=species traits related to dispersal X3=species characteristics (abundances and richnness) X4= xy spatial coordinates
The problem is that I have repeated measures design (longitudinal data). I treated "Time" (factor with 4 levels) as fixed factor in the main analysis (relating community dissimilarity matrix with environmental characteristics) and checked for sphericity etc. Now, I would like to see what proportion of variation is due to these 4 explanatory tables, but I am not sure how to deal with spatial coordinates (X4), without having to pool all data across dates? Should I just repeat xy data 4 times in the table? Should I do pcnm with X4, before using it in varpart? I would greatly appreciate any help, Vesna -- View this message in context: http://r.789695.n4.nabble.com/varpart-with-repeated-measures-tp3732631p3732631.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ [email protected] 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.
