Hi,

Thank you for the response.  I guess my question still is unanswered to a
certain degree.  I can see now that mean centering changes the
interpretation of the intercept and doing this is probably up to whomever
is performing the analysis.  In my situation, interpreting an intercept at
0 dB ambient noise doesn't make much sense because that value probably
doesn't exist anywhere.  However, with my particular situation where I have
a fixed effect that has 12 levels with all 59 individuals divided up
unevenly within those levels, is grand mean centering the way to go or is
group mean centering?  I suppose I can't decide because I can't imagine a
scenario where group mean centering would be warranted.  With either group
mean or grand mean, all that is being done is subtracting a mean from a
value, correct?  I've read some papers (Kontiainen et al. 2009) where they
did this and then divided the result by the overall standard deviation of
that variable.

Thank you!


On Fri, Aug 23, 2013 at 9:13 PM, Joshua Wiley <jwiley.psych@gmail.com>wrote:

> 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]]

