If you want to know if your model fit will your data, then looking at
residual-type plots is useful, as can be plotting the model-predictions and
observed data together. You might also find interesting "R2 statistics for
mixed models" by Matthew Kramer. Beware--as others have indicated, there
is
Hi Chiara,
If you just want to compare model fit, you could use a LRT between
models where you do and do not estimate the variance/covariance matrix
of random effects.
R^2 in mixed models do not have the same nice properties they do in
fixed effects models.
Cheers,
Josh
On Mon, Apr 30, 2012 at
As you have not provided a clue of what your models are, one can only guess.
But if you mean using lots of fixed effects vs a random effect, the answer is
that there is no such animal. They are two different non-nested models, and
should be chosen based on subject matter considerations. Standar
Goodmorning everybody,
i'm an italian statistician and i'm using R for research.
Could someone tell me some indices to see the goodness of fit in multilevel
modelling?
I'm using the lmer function, and I want to know if my model fit well my
data.
I actually want to justify the use of multilevel
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