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

[Alex Fine]

Just given the latency to first response, I'll give you my best guess:  
as far as getting an R^2 measure (=percentage of variance explained) 
from a multilevel model, I know of this:  
http://hlplab.wordpress.com/2009/08/29/nagelkerke-and-coxsnell-pseudo-r2-for-mixed-logit-models/

As far as getting R^2 for an individual predictor X1, assuming the 
pseudo R^2 measure you get from the method described in the blog post 
behaves the same as a regular R^2 measure, you could simply take the 
pseudo R^2 from a model with X1 + X2 and subtract from that the pseudo 
R^2 of a model with just predictor X2.  That should give you the portion 
of R^2 accounted for by just X1 (i.e. the so-called semi-partial of X1).

alex

Xiao He wrote:
> Hi dear R-lang experts and users,
>
> I have a question regarding the % of total variance account for by 
> individual predictors.
>
> In some articles I've read that mention regression analysis (mainly in 
> the 2nd language acquisition literature), the authors often mention 
> the percent of variance is explained or accounted for by each 
> independent variable. In Baayen (2008, in chapter 7), he discusses 
> that total variance accounted for by predictors of interests can be 
> obtained by abstracting variance of random effects from the variance 
> obtained in the model fitted with predictors of interests.
>
> I wonder however if there is any way to tease apart the contribution 
> made by each individual variance. In other words, what % of total 
> variance is accounted for by each predictor? 
>
> Thank you in advance for your help!
>
>
>
> Best,
> Xiao
>
>
>
>