[R] question about capscale (vegan)
Alicia Amadoz
Alicia.Amadoz at uv.es
Mon Nov 27 15:37:48 CET 2006
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
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