I used generalized linear mixed models (with the
glmmADMB package) to identify environmental factors related to parasite abundance in rodents. I used stepwise backward elimination to sequentially simplify the full model until only significant factors and interaction terms remained (Crawley, 2007). However, when I checked the final model's summary, one of the factors is no longer significant (
p = 0.087). However, in the ANOVA tests that led to the final model, this variable (temperature) is significant:
Final model's summary:
Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.131658 0.213630 15.33 < 2e-16 temperature -0.004019 0.0033143 -1.71 0.08673 .
Model 1: parasites ~ age + vegetation + synchrony Model 2: parasites ~ temperature + age + vegetation + synchrony NoPar LogLik Df Deviance Pr(>Chi) 1 7 -550.75 2 8 -546.81 1 7.898 **0.004949**
My questions are which significance value should I report? Does this mean that temperature is not important? I've seen manuscripts that report values from final's model summary but also some that report significance values from model comparisons, which one is correct?