[R-lang] Re: % of total variance accounted for by each predictor?

[Daniel Ezra Johnson]

> To calculate R2s for linear mixed models you only need to correlate the
> predictions against the actual outcome and square the results (=cor(outcome,
> fitted(lmer(outcome ~ .....)))^2). But be careful in the interpretation of
> the numbers. Most likely most of the variance the model captures will be due
> to the random participant intercepts. It's not uncommon that for, e.g. an R2
> of 50%, 45%+ of that come from the random intercept, while the remaining
> percent are due to the combined effect of the fixed effect predictors.

Note: although some people do suggest the above calculation, the
literature on "what is the best analog of R2 in a linear mixed model?"
is replete with many much more complicated alternative formulas,
tempered by healthy doses of the "don't do it at all" opinion
(sometimes from the same people).