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]] >> >> _______________________________________________ >> R-sig-phylo mailing list - R-sig-phylo@r-project.org >> <mailto:R-sig-phylo@r-project.org> >> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >> Searchable archive at >> http://www.mail-archive.com/r-sig-phylo@r-project.org/ >> > [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/