Hello Jari,
It is a little bit confusing. If A*B unfolds in A+B+A:B then A:B is the
real interaction component.
So, which if the code below will test the variance for the interaction
alone?
> adonis2(t(otu_fungi_out) ~ *Stage : Growhouse*, data=metadata_fungi_out,
by = "margin", permutations=9999)
Permutation test for adonis under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 9999
adonis2(formula = t(otu_fungi_out) ~ Stage:Growhouse, data =
metadata_fungi_out, permutations = 9999, by = "margin")
Df SumOfSqs R2 F Pr(>F)
Stage:Growhouse 3 1.0812 0.23075 1.9998 1e-04 ***
Residual 20 3.6045 0.76925
Total 23 4.6857 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> adonis2(t(otu_fungi_out) ~ *Stage * Growhouse*, data=metadata_fungi_out,
by = "margin", permutations=9999)
Permutation test for adonis under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 9999
adonis2(formula = t(otu_fungi_out) ~ Stage * Growhouse, data =
metadata_fungi_out, permutations = 9999, by = "margin")
Df SumOfSqs R2 F Pr(>F)
Stage:Growhouse 1 0.2171 0.04633 1.2045 0.2443
Residual 20 3.6045 0.76925
Total 23 4.6857 1.00000
>
The results is clearly very different. Also, in a normal adonis call I
didn't have any significance for the interaction that I have instead if I
use A:B. So ~ A*B will not test for interactions at all?
> *adonis*(t(otu_fungi_out) ~ Stage * Growhouse, data=metadata_fungi_out,
permutations=9999)
Call:
adonis(formula = t(otu_fungi_out) ~ Stage * Growhouse, data =
metadata_fungi_out, permutations = 9999)
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Stage 1 0.4877 0.48769 2.7060 0.10408 0.0247 *
Growhouse 1 0.3765 0.37647 2.0889 0.08034 0.0542 .
Stage:Growhouse 1 0.2171 0.21708 1.2045 0.04633 0.2507
Residuals 20 3.6045 0.18023 0.76925
Total 23 4.6857 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
Thank you!
Gian
On Tue, 16 Oct 2018 at 08:54, Jari Oksanen <jari.oksa...@oulu.fi> wrote:
>
>
> On 16/10/18 11:23, Torsten Hauffe wrote:
> > "adonis2(speciesdataset~A*B, by="margin") but then only the effect of
> the
> > interaction is tested."
> >
> > This is not entirely correct.
> > adonis2(speciesdataset~A:B, by="margin") would test the interaction
> alone.
> > ~A*B unfolds to ~A+B+A:B
>
> Well, it was correct: the only **marginal** effect in ~A+B+A:B is A:B (A
> and B are not marginal), and by = "margin" will only analyse marginal
> effects.
>
> Cheers, Jari Oksanen
> >
> > On Tue, 16 Oct 2018 at 11:51, Ellen Pape <ellen.p...@gmail.com> wrote:
> >
> >> Hi all,
> >>
> >> I don't know whether this is the correct mailing group to address this
> >> question:
> >>
> >> I would like to perform a 2-way permanova analysis in R (using adonis in
> >> vegan). By default you are performing sequential tests (by="terms"), so
> >> when you have 2 or more factors, the order of these factors matter.
> >> However, since I wanted to circumvent this, I chose for the option
> >> by="margin" (adonis2(speciesdataset~A*B, by="margin")) but then only the
> >> effect of the interaction is tested. On the "help page" of anova. cca it
> >> says: "if you select by="margin" -> the current function only evaluates
> >> marginal terms. It will, for instance, ignore main effects that are
> >> included in interaction terms."
> >>
> >>
> >> My question now is: can I somehow get the main effects tested anyhow,
> when
> >> the interaction term is not significant?
> >>
> >> Thanks,
> >> Ellen
> >>
> >> [[alternative HTML version deleted]]
> >>
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> >>
> >
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