Hi,

Mean centering will only change the interpretation of the intercept, and
could be done easily using (for example):

scale(mtcars$mpg, scale=FALSE)

Group mean centering, so that each individual has mean 0 changes things as
this is no longer just adding a constant.  A higher score then is relative
to each individuals' mean, not to an absolute value.

This can be accomplished using:

mtcars <- within(mtcars, {
  gmmpg <- ave(mpg, cyl, FUN = function(x) x - mean(x))
})

Cheers,

Joshua



On Fri, Aug 23, 2013 at 5:43 PM, AvianResearchDivision <segerfan83@gmail.com
> wrote:

> Hi all,
>
> I have a data set that includes 1 predictor variable (ambient noise) that
> is continuous and 2 predictor variables that are factors.  One of the
> predictor variables has 12 levels, that has 59 individuals (random effect)
> grouped within (unevenly).  The question is, should I consider mean
> centering my continuous predictor variable?  I know that interpretation of
> the model results means that the intercept and slope are based on ambient
> noise of 0 dB.  If I do want to mean center my data, is there a specific
> way I should do this because of the predictor variable that has individuals
> grouped within?  Thank you for your help.
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com

	[[alternative HTML version deleted]]

