Multi-level qualitative (fixed-effects) factors
Dear List, For the analysis of my GLMM i am using AIC values rather than stepwise regression to simplify it. I have developed some candidate models and am running through them now. I know a priori that there are some important interactions and i have also removed all the factors i consider unimportant. I have many multi level factors i.e habit - aquatic, terrestrial, epiphyte etc I ran the model with habit as a factor
model111 <-lmer(threatornot~1+(1|a/b) + habit, family=binomial)
Generalized linear mixed model fit by the Laplace approximation
Formula: threatornot ~ 1 + (1 | order/family) + habit
AIC BIC logLik deviance
1406 1436 -696.9 1394
Random effects:
Groups Name Variance Std.Dev.
family:order (Intercept) 6.9892e-01 8.3602e-01
order (Intercept) 4.2292e-14 2.0565e-07
Number of obs: 1116, groups: family:order, 43; order, 9
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04803 0.19174 -0.250 0.80219
habit2 1.10627 0.41607 2.659 0.00784 **
habit3 0.92578 0.78141 1.185 0.23611
habit4 0.14383 0.38477 0.374 0.70856
--- Which had a AIC of 1406 I then re-ran the model with only aquatic and got a lower AIC value - which i guess is to be expected as aquatic is highly significant and aquatic species are more prone to threat ( my response).
model112 <-lmer(threatornot~1+(1|a/b) + aquatic, family=binomial) model112
Generalized linear mixed model fit by the Laplace approximation
Formula: threatornot ~ 1 + (1 | order/family) + aquatic
AIC BIC logLik deviance
1395 1415 -693.4 1387
Random effects:
Groups Name Variance Std.Dev.
family:order (Intercept) 0.60007 0.77464
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) 0.1572 0.1827 0.860 0.389613
aquatic -0.6683 0.1737 -3.847 0.000119 ***
My question is - when i developed the candidate models i thought using multilevel factors would be OK and i would be able to tease out the individual levels. If i split the factors into levels from the beginning then i am left with a huge amount of candidate models? This would not be a problem in stepwise regression as i could just remove the habit with the least significant P Value. If i remove habits i "feel" are unimportant from the beginning i feel i would be limiting the model too much. I hope this makes sense! Has anyone else had this problem or can see a work around? Thanks Peter