[R-sig-ME] linear mixed models explained variances

[Lorin Raats]

Dear all,

I am a student and currently working with linear mixed models in my master thesis. So far, I haven’t been able to find anyone who could help solve my questions, so I hope some of you have the time to look at them.

I have made models, which are of the structure:

           fit18=lmer(recru$observed~recru$predicted*total$MMIdata+(1|total$nursery))

First of all, I would like to determine the significance of the model. So far I have only been able to determine the significance of the separate factors. But I have read that p-values don’t really work with linear mixed models. So how can I find the significance of the model?

Second of all, I would like to determine the variance explained by the separate factors, I have so far:


  1.  Using r.squaredGLLM(fit18) from the MuMIn package, you get a conditional and a marginal R². I have taken the conditional as the variance explained by my whole model. And I have taken the marginal as the variance explained by the fixed effects. Is this correct or did I make false assumptions?
  2.  Can I assume that the variance explained by the random effects is just the subtraction of conditional and marginal?
  3.  As I have three fixed factors in my model (two + interaction effect), I would like to see how much each of the fixed variables explains. I have followed a method I found online, but I am not that sure about the validity of this method. What I have used is:
                      fixedvaiance1= whole variance*fvariance1/(rvariance+fvariance1+fvariance2)

               (with r standing for random effect and f for fixed effect.)


                      If this method is false, what method an I follow to find the variance explained by each of my separate fixed factors?

I would like to thank you very much for having a look at my questions.

Kind regards,

Lorin Raats


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