Dear List, I was after some advice on model selection,
I am using AIC model selection rather than P-value based stepwise regression as i feel it is more robust (Burnham & Anderson, 2002). However there seems to be a huge difference in my results. The factors with the highest p-values , and therefore retained in the MAM, when i did an explanatory stepwise regression, do not appear in the model with the lowest AIC value - do the two approaches generally not match? The factors retained by the MAM are theoretically what i would expect, so i am a bit surprised as to why the model with the lowest AIC doesn't contain them? I have ranked the AIC models with Akaike weights, but still the top ranked models don't incorporate the traits i would expect / retained in the MAM. LOWEST AIC MODEL model43 <- lmer(threatornot~1+(1|order/family) + geophyte + seasonality + pollendispersal + woodyness, family=binomial) > model43 Generalized linear mixed model fit by the Laplace approximation Formula: threatornot ~ 1 + (1 | order/family) + geophyte + seasonality + pollendispersal + woodyness AIC BIC logLik deviance 1395 1430 -690.6 1381 Random effects: Groups Name Variance Std.Dev. family:order (Intercept) 0.37447 0.61194 order (Intercept) 0.00000 0.00000 Number of obs: 1116, groups: family:order, 43; order, 9 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.40234 0.43237 0.931 0.35208 geophyte2 0.06453 0.19616 0.329 0.74218 seasonality2 -1.06900 0.34241 -3.122 0.00180 ** pollendispersal2 0.64474 0.31089 2.074 0.03809 * woodyness2 0.47599 0.25646 1.856 0.06346 . BEST STEPWISE MAM Generalized linear mixed model fit by the Laplace approximation Formula: threatornot ~ 1 + (1 | order/family) + breedingsystem * fruit + woodyness AIC BIC logLik deviance 1409 1454 -695.3 1391 Random effects: Groups Name Variance Std.Dev. family:order (Intercept) 0.52475 0.7244 order (Intercept) 0.00000 0.0000 Number of obs: 1116, groups: family:order, 43; order, 9 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.1290 0.4909 -2.300 0.0215 * breedingsystem2 0.8123 0.4756 1.708 0.0876 . breedingsystem3 0.9449 0.5246 1.801 0.0717 . fruit2 1.3885 0.6221 2.232 0.0256 * woodyness2 0.5484 0.2627 2.088 0.0368 * breedingsystem2:fruit2 -1.6218 0.6577 -2.466 0.0137 * breedingsystem3:fruit2 -1.6645 0.7449 -2.235 0.0255 * The breedingsystem* fruit interaction, should, based on theory be important so why is it not in the model with the lowest AIC but is in the MAM? I am not sure if it is because i did not set out my candidate models correctly, I did a different model for every combination of traits (2 to the power of 7) -1 as i was unsure of which models would be important. I was given the data, i didn't collect it, therefore i have to work with what i have. Any advice would be greatly appreciated. [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology