I want to use linear mixed effect model analysis in R (package lme4). I have three fixed factor and one random factor. So, how can I achieve coefficient of random factor and R2 value of the model? I have studied different tutorial files but there were not coefficient of random factor and R2 value in their outputs.
You can get the R² of the model using the MuMin package and its function r.squaredGLMM() which returns two (pseudo) R²s, The Marginal R2 (the variance explained by fixed factors) and the Conditional R2 (variance explained by both fixed and random factors). You can then deduce the variance explained by the random factors alone.
All the best :)
https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf
Dear Niklas Schandry
I am not interested in the effect of random factor but I think that we should have coefficients for both of fixed and random factor in the regression equation. and what about R2 value of the model?
Yes, I have tried ranef() before. but in this way we have an intercept for each level of the random factor. but I think that we should have one coefficient for each random factor.
whats your idea about it?
Maybe you can find something useful in this link on random effects modeling
An intercept (or slope, or whatever) for each level of the random factor is all you could get. It's not possible to have one single coefficient for the whole factor when the factor has many levels, just as it's not possible to have one single coefficient for any other polytomous factor.
Once you have fitted your model, you can extract the coefficients with a few functions. have you tried summary() and coeff()?
If you are unsure how to get your results in R, you can always look into your R-objects. You can use ls() or str() to show you what is inside your model. I.e. model
You can get the R² of the model using the MuMin package and its function r.squaredGLMM() which returns two (pseudo) R²s, The Marginal R2 (the variance explained by fixed factors) and the Conditional R2 (variance explained by both fixed and random factors). You can then deduce the variance explained by the random factors alone.
All the best :)
https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf
It is worth emphasising that you can't get R^2 from a linear mixed model (or indeed from a generalized linear model that doesn't reduce to a general linear model). In these models the variance is not fixed but variations as a function of x or the hierarchical structure. Consequently the numerator for the R^2 calculation is not a constant.
Various pseudo-R^2 statistics and analogous statistics have been proposed but none have all the properties of R^2 (and as far as I understand they can not in principle do so).
This matters because you might get a statistic that is labelled R^2 but that doesn't behave as you would expect and may not always be reasonably interpreted that way.
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