Hi all,

I have posted a related post a few days ago but I am afraid I was not clear
enough. Here is a new explanation of what I am intending to do.
My aim is to assess the between-individual variability of both intercept
and slope for different variable factors.

To give you more details in my analyses, here is my main model:
mod<-lme(logitprop_vig~logdensity+logbiomass+position,random=~1|Identity,
data=tabimpala)

The response variable is the proportion of time spent in vigilance
(logit-transformed) and the explanatory variables are the density of
neighbors (continuous variable), the biomass (continuous variable) and the
individual position (categorical variable).

I would like to assess the inter-individual variability in intercept and
slope for each of those explanatory variables. Besides, to facilitate the
interpretation of the results, it would be convenient to work on
centered/standardized coefficients such as both  Vi (between-individual
variability in intercept) and Vs (between-individual variability in slope)
vary between 0 and 1, associated with their Confidence Interval.

 I thus would like to discuss with you how I can obtain those standardized
values. Do I have to standardize all the variables before running the
model? What would be the best option to do so? Is it not a problem to have
transformed variables in my model?
 Briefly, I know what I would like to obtain but I am not sure  how to do
it... Any help would be appreciated!


Tommy Gaillard
Intern student  CNRS
Burgundy University
Master 2 Research
Email: tommy.gaillard40@gmail.com

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