On Wed, 2013-04-24 at 13:13 -0700, Rémi Lesmerises wrote:

> Thank you!

>

> A last question: Is it still the same explanation if I remove the

> condition "first=TRUE" (and then testing for all axis) and permutest

> gives the same result?

No; again `permutest(...., first = FALSE)` and `envfit()` are testing

different models/fits but how they are different is not the same as with

your previous question.

`permutest(...., first = FALSE)` is testing whether the variance

explained by the k environmental variable (k = 4 I believe in your case)

is sufficiently large as to be unusual when compared to the variance

explained under a null model (achieved by permuting residuals,

propagating those residuals plus fitted values to give new data and

refitting the model on those new data). `envfit()` is doing what it

always did; assessing whether the correlation between the vector and the

2-d configuration is unusually large.

In the `permutest(...., first = FALSE)` case, you have a model with 4df.

In the `envfit()` case you have 4 models each with 1 degree of freedom.

The two methods also differ in terms of the role played by the

environmental data. In the `permutest` case, the environmental data are

the predictors. In the `envfit` case the env data are the responses and

the axis scores in the 2 dimensions chosen are the predictors.

HTH

G

> Rémi Lesmerises, biol. M.Sc.,

> Candidat Ph.D. en Biologie

> Université du Québec à Rimouski

>

[hidden email]
>

>

>

> On Wed, 2013-04-24 at 10:38 -0700, Rémi Lesmerises wrote:

> > Dear all,

> >

> > I did a RDA and when I looked to the signification of the test with

> > permutest, the output was non-significant. But when I used the envfit

> > function, some of the vectors are significant. All the test's

> > conditions are respected. What it means? Is it an error in the script?

>

> The two functions are testing two fundamentally different things.

> `permutest` (as you invoked it --- `first = TRUE`) is testing the first

> axis of the RDA. That axis is a linear combination of the constraints.

>

> The `envfit` procedure is testing the individual correlations between

> the 2-d configuration of samples in the RDA space and the direction of

> maximal variance of the environmental data. Each variable is considered

> separately.

>

> I can easily imagine a case, where the variance explained on the first

> axis is not significant but variance over 2 axes is significant, as one

> where the vectors do not point solely along the first RDA axis but also

> point along the 2nd axis. By looking only at their contributions to the

> first axis and summing them you don't explain a whole lot, but when you

> look at the directions in 2D space each variable explains a significant

> amount of variance.

>

> HTH

>

> G

>

> > Commands and output:

> >

> > > permutest(rda.ind, perm=999, first=TRUE)

> >

> > Permutation test for rda

> >

> > Call: rda(formula = x ~ ARH_frs + CON_frs + PRP_frs + RUI_frs +

> > VAM_frs, data = z)

> > Permutation test for first constrained eigenvalue

> > Pseudo-F: 4.093568 (with 1, 10 Degrees of Freedom)

> > Significance: 0.413

> > Based on 999 permutations under reduced model.

> >

> > > fit <- envfit(rda.ind, z, perm = 999, display = "lc")

> > > fit

> >

> > ***VECTORS

> >

> > RDA1 RDA2 r2 Pr(>r)

> > VAM_frs 0.145281 -0.989390 0.2388 0.147

> >

> > ARH_frs -0.876494 -0.481413 0.6127 0.002 **

> > CON_frs 0.904278 0.426944 0.4846 0.013 *

> > PRP_frs -0.997634 0.068755 0.9433 0.001 ***

> > RUI_frs -0.648512 -0.761204 0.6243 0.004 **

> > ---

> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> > P values based on 999 permutations.

> >

> >

> > Rémi Lesmerises, biol. M.Sc.,

> > Candidat Ph.D. en Biologie

> > Université du Québec à Rimouski

> >

[hidden email]
> >

> > [[alternative HTML version deleted]]

> >

> > ______________________________________________

> >

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