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19
votes
3answers
14k views

Comparing non nested models with AIC

Say we have to GLMMs mod1 <- glmer(y ~ x + A + (1|g), data = dat) mod2 <- glmer(y ~ x + B + (1|g), data = dat) These models are not nested in the usual ...
39
votes
5answers
73k views

Negative values for AICc (corrected Akaike Information Criterion)

I have calculated AIC and AICc to compare two general linear mixed models; The AICs are positive with model 1 having a lower AIC than model 2. However, the values for AICc are both negative (model 1 ...
6
votes
1answer
2k views

Linear mixed effects model and - multiplicity issue and adjusting for p-values

In our randomized controlled trial, we used linear mixed effects models to test differences between groups in changes from baseline to six months while adjusting for important covariates. We ran ...
24
votes
5answers
3k views

What is the upside of treating a factor as random in a mixed model?

I have a problem embracing the benefits of labeling a model factor as random for a few reasons. To me it appears like in almost all cases the optimal solution is to treat all of the factors as fixed. ...
31
votes
1answer
74k views

Negative values for AIC in General Mixed Model [duplicate]

I'm trying to select the best model by the AIC in the General Mixed Model test. The best model is the model with the lowest AIC, but all my AIC's are negative! So is the biggest negative AIC the ...
22
votes
2answers
9k views

How should mixed effects models be compared and or validated?

How are (linear) mixed effects models normally compared against each other? I know likelihood ratio tests can be used, but this doesn't work if one model is not a 'subset' of the other correct? Is ...
2
votes
2answers
3k views

Relative variable importance values vs. magnitude of effect

I have ran a series of models to see which best fit the response variable and I got the following (for the model average of all models with a $\Delta AIC < 2$). I am currently learning models so ...
6
votes
1answer
396 views

How can mixed-effect and fixed-effect generalised linear models be compared using BIC?

Assuming I have two generalised linear models, one with only fixed effects and another with fixed and random effects, how can I compare which model is most parsimonious using AIC/BIC? I can only fit ...
3
votes
2answers
6k views

The 'best' model selected with AICc have lower $R^2$ -square than the full/global model

I have used the R lme function (nlme package) to construct linear mixed models, with a single random effect (as a random intercept) and a varIdent variance structure on a fixed effect (that is a ...
9
votes
2answers
10k views

AIC, anova error: Models are not all fitted to the same number of observations, models were not all fitted to the same size of dataset

I have models like this: ...
1
vote
0answers
281 views

Fitting an HLM model in lme4

I'm a fairly inexperienced statistician fighting a huge deadline and just need some peace of mind that I'm not making a massive error here. I'd be most grateful for pointers. I've been playing ...
1
vote
1answer
53 views

Overfitting model or issue of categorical predictors?

Is it possible to overfit a model by virtue of having too many categorical variables? I have 3 categorical variables and my dependent measure is continuous (or a ratio I guess, I'm measuring ...
1
vote
0answers
196 views

Mixed linear models with exactly the same AIC?

I have a dataset in which I measure the response variable of a number of individual animals. I used this response variable as the dependent variable in a linear mixed model, for which I have 5 ...