I am currently using the R package lme4.
I am using a linear mixed effects models with random effects:
library(lme4) mod1 <- lmer(r1 ~ (1 | site), data = sample_set) #Only random effects mod2 <- lmer(r1 ~ p1 + (1 | site), data = sample_set) #One fixed effect + # random effects mod3 <- lmer(r1 ~ p1 + p2 + (1 | site), data = sample_set) #Two fixed effects + # random effects
To compare models, I am using the
anova function and looking at differences in AIC relative to the lowest AIC model:
anova(mod1, mod2, mod3)
The above is fine for comparing models.
However, I also need some simple way to interpret goodness of fit measures for each model. Does anyone have experience with such measures? I have done some research, and there are journal papers on R squared for the fixed effects of mixed effects models:
- Cheng, J., Edwards, L. J., Maldonado-Molina, M. M., Komro, K. A., & Muller, K. E. (2010). Real longitudinal data analysis for real people: Building a good enough mixed model. Statistics in Medicine, 29(4), 504-520. doi: 10.1002/sim.3775
- Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., & Schabenberger, O. (2008). An R2 statistic for fixed effects in the linear mixed model. Statistics in Medicine, 27(29), 6137-6157. doi: 10.1002/sim.3429
It seems however, that there is some criticism surrounding the use of measures such as those proposed in the above papers.
Could someone please suggest a few easy to interpret, goodness of fit measures that could apply to my models?