[R-sig-eco] repeated measures NMDS?

Gavin Simpson gavin.simpson at ucl.ac.uk
Thu Nov 11 10:16:09 CET 2010 

On Thu, 2010-11-11 at 09:50 +0100, Kay Cecil Cichini wrote:
> gavin,
> 
> sorry - of course it should be permute.strata=F, permuting within 
> individual sites!
> but despite of this the code should work, doesn't it?

Yes, it should - i.e the permutation will be random within the blocks.
Whether it does or not is another matter entirely. AFAICR, this option
in permuted.index2() did work.

*But*, this is doing exactly what the original permuted.index() does if
you set argument 'strata' to be the grouping factor - in your case:

envfit(sol ~ env, strata = rep.mes, perm = 999)

for example. So there is no need to code up the analysis by hand.

This of course doesn't account for any temporal correlation within sites
nor, if the observations on the 24 blocks were made at the same times,
that you might want to have the same permutation within each block.

In the former there are 3^24 possible permutations (time series within
blocks), so 999 random perms is reasonable, *but* some of these random
perms (in permuted.index()) will not respect the temporal ordering and
thus you aren't really exploring the correct NULL.

With the latter constraint (same temporal perm within blocks), there are
3 random permutations, so good luck getting a reasonable p-value from
that.

The two restricted permutations /should/ work correctly, /but/ if you
are doing this by hand, I'd look at the functions in the 'permute'
package - only on R-Forge, on the Vegan R-Forge area - as I know the
code to generate these permutations in that package *does* work. (It is
the helper cruft around it that needs more work.)

https://r-forge.r-project.org/R/?group_id=68

I've had a busy Summer and not made as much progress as I would have
liked, but I hope to finish this soon and make an initial release to
CRAN so we can start grafting it into vegan.

In the meantime, I can help people try to link the two packages if
needed, but I don't have much time till the end of this month.

G

> thanks,
> kay
> 
> 
> 
> Gavin Simpson schrieb:
> > On Wed, 2010-11-10 at 23:33 +0100, Kay Cecil Cichini wrote:
> >> hi eduard,
> >>
> >> i faced similar problems recently and came to the below solution.
> >> i only try to address the pseudoreplication with an appropiate  
> >> permutation scheme.
> >> when it comes to testing the interactions, things may get more complicated.
> >>
> >> the code is in no way approven of, but at least it maybe good enough  
> >> for a starter.
> >>
> >> best,
> >> kay
> > 
> > Hi Kay,
> > 
> > I don't think you have this right.
> 
> > 
> > If you have measured repeatedly, say 5 times, on the same 10
> > individuals, or if you have ten fields and you take 5 quadrats from
> > each, you need to permute *within* the individuals/fields, not permute
> > the individuals/fields which is what permute.strata does. permute.strata
> > would be useful in evaluating factors that vary at the block
> > (individuals/fields) level, not at the sample levels.
> > 
> > From what Eduard and you describe, the code you show is not the correct
> > permutation. But I may have misunderstood your intention.
> > 
> > Also, be careful with permuted.index2 - there are reasons why it hasn't
> > been integrated (design goals changed and we felt it would work best in
> > a separate package that others could draw upon without loading all of
> > vegan) and the code has festered a bit and may contain bugs - buyer
> > beware!
> > 
> > G
> > 
> >> library(vegan)
> >>
> >> ### species matrix with 5 sp.
> >> ### one env.variable
> >> ### a factor denoting blocks of repeated measurments
> >>
> >> sp<-matrix(runif(24*3*5,0,100),24*3,5)
> >> env<-rnorm(24*3,10,2)
> >> rep.mes<-gl(24,3)
> >>
> >> ### NMDS:
> >> sol<-metaMDS(sp,trymax=5)
> >>
> >> fit<-envfit(sol~env)
> >> plot(sol)
> >> plot(fit)
> >>
> >> ### testing code for appropiate randomization,
> >> ### permuting blocks of 3 as a whole:
> >> permuted.index2(nrow(sp),permControl(strata = rep.mes,permute.strata=T))
> 
> correctly, this should say:
> ### testing code for appropiate randomization,
> ### permuting within sites:
> permuted.index2(nrow(sp),permControl(strata = rep.mes))
> 
> >> B=4999
> >>
> >> ### setting up frame for population of r2 values:
> >> pop<-rep(NA,B+1)
> >> pop[1]<-fit$vectors$r
> >>
> 
> and:
> >> ### loop:
> >> for(i in 2:(B+1)){
> >> fit.rand<-envfit(sol~env[permuted.index2(nrow(sp),
> >>                           permControl(strata = rep.mes))])
> >>            pop[i]<-fit.rand$vectors$r
> >> }
> >>
> >> ### p-value:
>  >> print(pval<-sum(pop>pop[1])/B+1)
> 
> here a bracket was missing:
> print(pval<-sum(pop>pop[1])/(B+1))
> 
> >>
> >> ### compare to anti-conservative p-value from envfit(),
> >> ### not restricting permutations:
> >>
> >> envfit(sol~env,perm=B)
> >>
> >>
> >> Zitat von Eduard Szöcs <szoe8822 at uni-landau.de>:
> >>
> >>> Thanks, that helped.
> >>>
> >>> permuted.index2() generates these types of permutations. But  
> >>> envfit() does not use this yet.
> >>> What if I modify vectorfit() (used by envfit() ) in such a way that  
> >>> it uses permuted.index2() instead of permuted.index()?
> >>>
> >>>
> >>> Eduard Szöcs
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> Am 08.11.2010 22:01, schrieb Gavin Simpson:
> >>>> On Mon, 2010-11-08 at 15:39 +0100, Eduard Szöcs wrote:
> >>>>> Hi listers,
> >>>>>
> >>>>> I have species and environmental data for 24 sites that were sampled
> >>>>> thrice. If I want to analyze the data with NMDS I could run metaMDS on
> >>>>> the whole dataset (24 sites x 3 times = 72) and then fit environmental
> >>>>> data, but this would be some kind of pseudoreplication given that the
> >>>>> samplings are not independent and the gradients may be overestimated,
> >>>>> wouldn`t it?
> >>>>>
> >>>>> For environmental data a factor could be included for the sampling
> >>>>> dates - but this would not be possible for species data.
> >>>>>
> >>>>> Is there an elegant way either to aggregate data before ordination or
> >>>>> to conduct sth. like a repeated measures NMDS?
> >>>>>
> >>>>> Thank you in advance,
> >>>>> Eduard Szöcs
> >>>> Depends on how you want to fit the env data - the pseudo-replication
> >>>> isn't relevant o the nMDS. If you are doing it via function `envfit()`,
> >>>> then look at argument `'strata'` which should, in your case, be set to a
> >>>> factor with 24 levels. This won't be perfect because your data are a
> >>>> timeseries and, strictly, one should permute them whilst maintaining
> >>>> their ordering in time, but as yet we don't have these types of
> >>>> permutations hooked into vegan.
> >>>>
> >>>> If you are doing the fitting some other way you'll need to include
> >>>> "site" as a fixed effect factor to account for the within site
> >>>> correlation.
> >>>>
> >>>> You don't need to worry about the species data and accounting for
> >>>> sampling interval. You aren't testing the nMDS "axes" or anything like
> >>>> that, and all the species info has been reduced to dissimilarities and
> >>>> thence to a set of nMDS coordinates. You need to account for the pseudo
> >>>> rep at the environmental modelling level, not the species level.
> >>>>
> >>>> HTH
> >>>>
> >>>> G
> >>>>
> >>> _______________________________________________
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> >>>
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> > 
> 

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