-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 05/19/2011 02:41 PM, Stolen, D Eric (KSC-IHA-4400)[Innovative Health Applications LLC] wrote: > Hello; I am working on a logistic regression model in which I have > quasi-complete separation on an explanatory variable (see table > below). The response variable is Success of parrot reintroductions, > and one of the explanatory variables is PredThreat, a 3 category > variable designating the level of predator threat to the population. > When I fit the univariate logistic regression model Success ~ > PredThreat, I get a huge standard error, which I believe is an > indication of the optimization algorithm failing due to > quasi-complete separation. I am testing a variety of models using > information-theoretic model selection to judge which variables are > important to reintroduction success. My question concerns what to do > to about PredThreat, since it appears to be an informative variable. > First I'm wondering if I can trust the AIC value calculated from the > model with PredThreat? Second, to get at an effect size and also to > include it in multivariate models, I thought of treating PredThreat > ! as a continuous variable. When I do that in the univariate model, > I get a more reasonable parameter estimate and standard error, but a > much lower AIC. I'd really appreciate any insights in how to deal > with this problem.
You might look into the brglm, logistf, and arm packages which all offer options for bias-reduced (or Bayesian) GLMs that should (?) do a better job with separation ... ? -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.10 (GNU/Linux) Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org/ iEYEARECAAYFAk3Vjj4ACgkQc5UpGjwzenOvrgCePLXdZ9hny71Suy4MHOvfRS3e r0gAn2Iw7XQziXKHEIPpIeZhhmNZSkCx =8Fty -----END PGP SIGNATURE----- _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology