Hi, I am a PhD student who is using mixed effect models (and R) for the first time so apologies if my question is a bit basic.  I also haven't used this forum much so if this topic has already been covered in depth in the past and there is an easy answer out there that I've missed then additional apologies!  

I am using a linear mixed model (structure as below) to predict total tree biomass (log.mass) using tree diameter (dbh) and height as explanatory variables, allowing the intercept and the slope of height to vary by my grouping factor (species_site).

Model:

lmer((log.mass)~centre.log.dbh+centre.log.height+(1+centre.log.height|species_site),data=data3,REML=T) 

This model structure was chosen as 'the best' primarily on the basis of AIC value.   Residual plots look fairly OK.

I would now like to check the predictive performance of my model (by cross-validation?) and wonder how to go about this in the context of a linear mixed effect model?.  Is there a package/function in R which deals with this? 

Any advice on how to proceed would be greatly appreciated!

Regards,

Rachel Cohen
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