What is marginal and conditional r squared?
Marginal and conditional are R2 values for generalized mixed-effects models calculated using the r. Marginal R2 provides the variance explained only by fixed effects and conditional R2 provides the variance explained by the entire model, i.e., both fixed effects and random effects.
What is conditional r squared?
The conditional R2 is the proportion of total variance explained through both fixed and random effects. The article by Nakagawa and Shielzeth goes on to expand these formulas to situations with more than one random variable, and also to the generalized linear mixed effects model (GLMM).
What is the marginal R2 of a mixed effect model?
The first is called the marginal R2 and describes the proportion of variance explained by the fixed factor(s) alone: The fixed-effects variance is in the numerator. The denominator is the total variance explained by the model, including (in order): the fixed-effects variance,…
What does marginal R2 close to 1 mean?
A marginal R2 close to zero tells us that the fixed effects aren’t explaining much variation, and a conditional R2 close to 1 tells us that most of that unexplained variation is between groups (people) rather than between observations within groups (people).
How to calculate marginal and condition values in R?
I’ve written a function in R called sem.model.fits (along with an amiable fellow named Juan Sebastian Casallas) that calculates the marginal and condition R 2 s, as well as AIC values for a complementary approach to model comparison.
Which is the second part of the R2 function?
The second is the conditional R2, which describes the proportion of variance explained by both the fixed and random factors: In this case, the numerator contains both the variance of the fixed effects, as well as the sum of random variance components for each level l of the random factor.