Hi Gavin, I have been analyzing real data (sorry but I am not allowed to post these data here) and what I got was this,
mydistmat_f.cap <- capscale(distmat_f ~ F + L + F:L, mfactors_frame) Warning messages: 1: some of the first 30 eigenvalues are < 0 in: cmdscale(X, k = k, eig = TRUE, add = add) 2: Se han producido NaNs in: sqrt(ev) > mydistmat_f.cap Call: capscale(formula = distmat_f ~ F + L + F:L, data = mfactors_frame) Inertia Rank Total 0.3758 Constrained 0.2110 4 Unconstrained 0.1648 4 Inertia is squared distance Some constraints were aliased because they were collinear (redundant) Eigenvalues for constrained axes: CAP1 CAP2 CAP3 CAP4 1.679e-01 2.954e-02 1.349e-02 1.233e-05 Eigenvalues for unconstrained axes: MDS1 MDS2 MDS3 MDS4 1.388e-01 2.601e-02 4.076e-05 2.064e-07 So, by these results I can tell that there are 4 axes that explain 0.1648 of the total variance and another 4 axes that explain 0.2110 of the total variance. But I don't understand the difference between constrained and unconstrained. > anova(mydistmat_f.cap) Permutation test for capscale under direct model Model: capscale(formula = distmat_f ~ F + L + F:L, data = mfactors_frame) Df Var F N.Perm Pr(>F) Model 4 0.21 1.2798 400.00 0.0875 . Residual 4 0.16 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > summary(anova(mydistmat_f.cap)) Df Var F N.Perm Pr(>F) Min. :4 Min. :0.1648 Min. :1.280 Min. :200 Min. :0.12 1st Qu.:4 1st Qu.:0.1764 1st Qu.:1.280 1st Qu.:200 1st Qu.:0.12 Median :4 Median :0.1879 Median :1.280 Median :200 Median :0.12 Mean :4 Mean :0.1879 Mean :1.280 Mean :200 Mean :0.12 3rd Qu.:4 3rd Qu.:0.1994 3rd Qu.:1.280 3rd Qu.:200 3rd Qu.:0.12 Max. :4 Max. :0.2110 Max. :1.280 Max. :200 Max. :0.12 NA's :1.000 NA's : 1 NA's :1.00 Then, I want to know the sum of squares of anova to check with other analysis that we performed but I can't see them by the output of anova. Besides, I am wondering if there is any manner to identify the main effects, factor effects and interaction in this anova analysis. I would be very grateful if you could help me to understand these results. Thank you very much, Alicia ______________________________________________ R-help@stat.math.ethz.ch 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.