[R-sig-ME] linear mixed models explained variances
lorin.raats at student.kuleuven.be
Wed Aug 16 11:26:55 CEST 2017
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:
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.
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