-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 11-01-13 04:38 AM, Chris Mcowen wrote: > > Dear list, > > I am modelling the effect of various life history traits of species > against their extinction rating. > > My data has a phylogentic signal (see table below) so to be > statistically correct i worked with phylogentic independent contrasts > > > From Purvis et al., 2000 "phylogenetic analyses were necessary > because of the pseudoreplication and, hence, elevated type I error > rates that result from treating species as independent points when > relevant variables show a phylogenetic pattern" > > Variable λ ( Pagel) IUCN extinction risk > 0.47 Breeding system > 0.99 Endosperm 0.96 Floral symmetry > 0.93 Fruit 0.97 Pollen > dispersal 0.99 Seasonality > 0.52 Storage organ 0.85 Woodyness > 0.61 > > There are various ways of doing this one method is to use the package > CAIC (generated using compara- tive analysis by independent > contrasts) which utilizes the phylogeny to generate the independent > contrasts. However as i was using it i found this: from Sodhi et al., > 2008 > >> It was necessary to decompose the variance across species by coding >> the random-effects error structure of the GLMM as a hierarchical >> taxonomic (class/order/ family) effect (Blackburn & Duncan, 2001). >> We had insufficient replication within genera to include the genus >> in the nested random effect. Our method is more appropriate than >> the independent-contrasts approach (Purvis et al., 2000) in >> situations where a complete phylogeny of the study taxon is >> unavailable, when categorical variables are included in the >> analysis, and when model selection, rather than hypothesis testing, >> is the statistical paradigm being used. > > So i gave this a go - using GLMM and setting the order / family as > random effects. > > I then came across mcmcGLMM which allows the use of a phylogentic > tree to deal with the phylogenetic structure of the data set. > > So i gave this a go as well! > > Finally to be consistent i ran the model with no phylogenetic control > - using GLM. > > My criteria for model selection was that of Burnham and Anderson - > using AIC and AICML etc to select the "most likely" set of models. > > Interestingly i found that using all four methods the same model > (based on AIC difference) was selected as the most likely? > Furthermore, the pattern of AIC differences across the models > reflected each other.
I think your comparison is really worthwhile, but I don't see why this is surprising at all -- perhaps I'm missing something. Given that you have reasonably strong effects of your predictors, reasonably minor differences in the way that you account for correlation won't change the qualitative conclusions. Ben Bolker > > I was wondering why this maybe? Does anybody know of similar studies > comparing model selection from GLM and GLMM? > > I apologise if this is not the correct forum to post this question. > > I have attached links to two graphs that explain my question. > > Thanks Chris > > LINKS > > https://files.me.com/chrismcowen/hmptnq > > files.me.com/chrismcowen/t3xvk5 > > _______________________________________________ R-sig-phylo mailing > list R-sig-phylo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.10 (GNU/Linux) Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org/ iEUEARECAAYFAk0vE44ACgkQc5UpGjwzenPPRwCYwge2SpvCiNaRQn7Q3NBW5NQM JwCfcdqvYMkrPyh7IOY8mRDJNil9Rzc= =oVYU -----END PGP SIGNATURE----- _______________________________________________ R-sig-phylo mailing list R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
