On Mon, 2006-11-27 at 15:37 +0100, Alicia Amadoz wrote: > 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)
I believe you can write that formula as: distmat_f ~ F * L > > 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) Sorry, I don't know enough about this method to know whether this a problem you should worry about or not. You should read up on the method some more to decide if the first warning is something you should be worried about. IIRC, negative eigenvalues are to be expected with this method as they are handled explicitly by capscale, and as this is a warning coming from cmdscale(), I suspect it is a helpful feature of that function, which you don't need to worry about when used in capscale(). > > > 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. The constrained axes are axes that are linear combinations of your explanatory variables (F, L and F:L), so this is the bit of your genomic data that is explained by those explanatory factors. The unconstrained bit is the remaining variance not explained, and are MDS (PCoord) axes. So you can explain c. 56% of the variance in your genomic data with F, L, and F:L. Note the warning about aliased constraints - this means that at least the variance of one variable in the model (inc interactions) is completely correlated with another variable (or combination of variables?) and is redundant. Type alias(mydistmat_f.cap) to see which coefficients are aliased and ?alias to see what this means. > > > 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. There isn't a summary method for anova.cca, and anyway, this anova isn't working on sums of squares, but on other measures of variance. It is a permutation test, and simply works out with brute force how likely you are to have a model explaining 56% of the total variance given your sample size and model complexity, under a null/random model. It sounds like you haven't grasped fully the fundamentals of the methods you are employing, and I would strongly advise you to do some more reading up on these methods. I can, at best, only guide you as I am not that familiar with the technique myself. A good start would be the refs in ?capscale and then search for papers that cite Anderson & Willis and that use the methodology. > > Thank you very much, > Alicia HTH G -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Gavin Simpson [t] +44 (0)20 7679 0522 ECRC & ENSIS, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ______________________________________________ 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.