Dear all,
A while ago, I was kindly advised to try MCMCglmm to investigate
invasion success of non-native species while accounting for phylogenetic
relatedness. I have managed to run some explanatory models but stumble
upon converge problems…
The data are (1) /introduction events/ of non-native species. There are
about 4.000 introduction events, but only about 40 have resulted in an
established population – so there is a low number of ‘events’.
Successful introductions are coded as “1”, failure is “0”, (2) the
/phylogenetic tree/ is an ultrametric majority-rule consensus tree
(class “phylo”, number of tips: 359, number of nodes: 298), (3)
/country/ in which species are introduced is included as a random
effect, (4) a set of continuous /explanatory variables/.
I have mainly explored univariate models (ie one explanatory variables
at a time), but including multiple explanatory variables also results in
converge problems. I use the following model, with continuous variable
‘CloseCentralInv’ as single explanatory variable.
prior.test<-list(R=list(V=1,nu=0.002), G=list(G1=list(V=1, nu=0.002),G2
= list(V=1, nu=0.002)))
test.phylo1 <- MCMCglmm(invasiveStatus ~ CloseCentralInv,
random = ~species + country,
ginverse=list(species=inv.mytree$Ainv),
nitt=1000000, thin=500, burnin=10000,
family = "categorical",
data = data.trade,
prior = prior.test,
verbose = FALSE)
As far as I understand, the basic model output looks reasonable (here
<https://www.dropbox.com/s/kffj8o3nf9nz0xn/summary%28test.phylo1%29.png?dl=0>,
1), although effective sample sizes for the random effects seem to be
small.
However, /Heidelberger and Welch's/ convergence diagnostic often passes
the stationary test, but not the Halfway mean test (example here
<https://www.dropbox.com/s/t25y37slvkqyd8l/heidel.diag.png?dl=0>, 2).
/Gelman and Rubin/'s convergence diagnostic (calculated on two chains)
indicates potential scale reduction factors that are often well above 1
(example here
<https://www.dropbox.com/s/kbxrc73ep1kjli5/gelman.diag.png?dl=0>, 3). A
plot of Gelman and Rubin also does not clearly indicate after how many
iterations convergence is to be expected (example here, 4). I tried to
use Raftery and Lewis's diagnostic to estimate the number of necessary
iterations, but this invariably outputs “You need a sample size of at
least 3746 with these values of q, r and s”. Traces for this model run
can be accessed here
<https://www.dropbox.com/s/7vvqf0r7she71hu/traces.png?dl=0> (5).
I tried upping the number of iterations to 1.000.000 for a number of
variables, but this does not seem to ameliorate the convergence problems.
I can see the following causes: (1) I misspecified the model and it is
not doing what I think it should be doing, (2) I actually need much more
iterations, (3) I need to specify stronger priors, (4) I am trying to
get blood from stone (aka the data do not allow such an analysis).
I might add that if I look at the p-values and estimates obtained
through the (non-converging- MCMCglmm runs, the results are very much
similar to a ‘simpler’ glmm where phylogeny is accounted for by using a
nested random effect for taxon and genus.
Any suggestions on how to proceed are much appreciated.
Best wishes and thanks in advance,
Diederik
1.
https://www.dropbox.com/s/kffj8o3nf9nz0xn/summary%28test.phylo1%29.png?dl=0
2. https://www.dropbox.com/s/t25y37slvkqyd8l/heidel.diag.png?dl=0
3. https://www.dropbox.com/s/kbxrc73ep1kjli5/gelman.diag.png?dl=0
4. https://www.dropbox.com/s/0e2unw7rmr7rt2y/gelman.plot.png?dl=0
5. https://www.dropbox.com/s/7vvqf0r7she71hu/traces.png?dl=0
On 6/3/2015 5:37 PM, Jörg Albrecht wrote:
> Hi Diederik,
>
> you can use MCMCglmm. The package allows for inclusion of phylogenetic
> information, random effects and zero-inflated response variables.
> However, it may take some time to get familiar with the package.
>
> Best,
>
> J
>
>
> —
> Jörg Albrecht, PhD
> Postdoctoral researcher
> Institute of Nature Conservation
> Polish Academy of Sciences
> Mickiewicza 33
> 31-120 Krakow, Poland
> www.carpathianbear.pl <http://www.carpathianbear.pl>
> www.globeproject.pl <http://www.globeproject.pl>
> www.iop.krakow.pl <http://www.iop.krakow.pl>
>
>> Am 03.06.2015 um 17:25 schrieb Diederik Strubbe
>> <diederik.stru...@uantwerpen.be <mailto:diederik.stru...@uantwerpen.be>>:
>>
>> Dear all,
>>
>>
>>
>> I am struggling with analysing a dataset aimed at explaining invasion
>> success of non-native species. At a country level, I need to relate
>> invasion success (binomial: 0 for failed invasions, 1 for success) to
>> socio-economic variables, taking into account
>>
>> - Phylogenetic relatedness among introduced species: including
>> a phylogenetic tree
>>
>> - Country as a random effect
>>
>> - The fact that data are zero-inflated (most introductions
>> fail).
>>
>>
>>
>> Any suggestions for R packages that can handle a binomial response
>> variable, phylogenetic trees, random effects and zero-inflation?
>>
>>
>>
>> Thanks in advance,
>>
>>
>>
>> Diederik
>>
>> --
>> Dr.Diederik Strubbe
>> Evolutionary Ecology Group
>> Department of Biology
>> University of Antwerp
>> Middelheimcampus GV310
>> Groenenborgerlaan 171
>> 2020 Antwerpen, Belgium
>> office: +32 3 265 34 69
>> mobile phone: +32 477445568
>> skype user name: lakrinn
>>
>>
>> [[alternative HTML version deleted]]
>>
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