Jacob,

Unfortunately the meaning of percent variance attributed to random terms (or to different "levels" if you're into that) is conceptually pretty unclear when there are random slopes. In general the variance of Y differs at different values of X. Consequently, estimates of percent variance explained are different for different values of X, and so they are not particularly useful or informative statistics in themselves. You may want to check out the following resources:

Sections 1 and 2 of Goldstein, Browne, & Rasbash, 2002:
http://www.bristol.ac.uk/cmm/research/pvmm.pdf

Slides 28, 29, and 30 from the following powerpoint presentation:
http://www.bristol.ac.uk/cmm/software/support/workshops/materials/random-slopes.pdf

Jake

> Date: Tue, 22 Apr 2014 16:34:41 -0400
> From: segerfan83@gmail.com
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Calculate % Variance Attributed to Random Slopes	(Phenotypic Plasticity)
> 
> Hi all,
> 
> I am trying to figure out how to calculate how strong individual plasticity
> is in my model:
> 
> model<-lmer(X~Y*Year+(Y|Individual)
> 
> but I do not know how to extract the proportion of variance explained by
> the individual slope term.  If I run the printout:
> 
> summary(model)
> 
> I can get the variance in slope with respect to Y, but this is not the
> variance in Y itself, but rather how X is influenced by Y.
> 
> Any ideas on how to get the variance in Y itself?
> 
> Thank you,
> Jacob
> 
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