search for: cca2

...Inertia Proportion Rank Total 5 1 Constrained 5 1 5 Unconstrained 0 0 0 Inertia is mean squared contingency coefficient Some constraints were aliased because they were collinear (redundant) Eigenvalues for constrained axes: CCA1 CCA2 CCA3 CCA4 CCA5 1 1 1 1 1 > plot(strain.cca) > summary (strain.cca) Call: cca(formula = strain.data ~ Ph + TotalN + Organicmatter + Ca + K + Na + P + Cu + Mn, data = env.data) Partitioning of mean squared contingency coefficient: Inertia Proportion...

...Inertia Proportion Rank Total 5 1 Constrained 5 1 5 Unconstrained 0 0 0 Inertia is mean squared contingency coefficient Some constraints were aliased because they were collinear (redundant) Eigenvalues for constrained axes: CCA1 CCA2 CCA3 CCA4 CCA5 1 1 1 1 1 > plot(strain.cca) > summary (strain.cca) Call: cca(formula = strain.data ~ Ph + TotalN + Organicmatter + Ca + K + Na + P + Cu + Mn, data = env.data) Partitioning of mean squared contingency coefficient: Inertia Proportion...

...nts using a 4th root transformation into count.dbf2 ? based on a suggestion from a colleague and following up in ?Quinn, G. P., and M. J. Keough. 2002. Experimental design and data analysis for biologists, 1st edition?. 3. I then used the following two commands to generate an ANOVA table: > out.cca2 = cca(count.df2 ~ Flood*(Fence+Fire+age)+topo, predictor.df) > anova(out.cca2, by="term", step=2000) 4. Since the order in which the factors are entered seems to matter, I tried a number of iterations obtaining similar results to: Model: cca(formula = response.df2 ~ Flood * (Fence +...

...################## #R code data<-read.table("y:directory\\sample.data",header=T) names(data) attach(data) library(vegan) # I multiplied up the volume because I thought that the issue may have been the fact that I had x.x numbers but I still got the same problem sumvol<-sum_vol*10000 cca2<-cca(sumvol~bioclim6+bioclim9+bioclim11) cca2 ###### output: Call: cca(formula = sumvol ~ bioclim6 + bioclim9 + bioclim11) Inertia Rank Total 0 Inertia is mean squared contingency coefficient ######################################################## -------------- next part ----...

...=DATA) and I used anova(CCA1, perm.max=499) to test the significance by means of Monte Carlo permutations under full model. The model was p<0.05 and the result of the plot was "good for my eyes", however, when I did summary(CCA1), the first two axis accounted 0.04 CCA1 and similar in CCA2....then the variation explained by each axis was small. On the other hand, when I performed CCA in CANOCO, without selecting the option Log-transforming data matrix and without downweighting rare species, the results were the opposite from the CCA performed in R. The axes accounted high percentage...