[R-sig-ME] interpretation of categorical crossed effect in lme4
Ken Beath
ken.beath at mq.edu.au
Sat Dec 6 21:23:38 CET 2014
The random effect for x2 is giving the variation in the effect of x2, that
is the difference in levels (from x2=0 to x2=1), with id.
I would first try the model, and see if it improves AIC.
y ~ x1 + x2 + (1 +x2 | group)
This now allows for the random effects for the intercept and x2 to be
correlated
On 7 December 2014 at 02:12, Andrew McAleavey <andrew.mcaleavey at gmail.com>
wrote:
> Hi,
>
> I have a lmer model of the form:
> y ~ x1 + x2 + (1 | group) + (0 +x2 | group) ;
> where x1 is continuous, x2 is dichotomous and dummy-coded, and group has
> about 250 levels (each with minimum 3 observations in each x2 level, but
> the average is more like 7 per x2 level, and over 15 observations per group
> on average, ignoring x2). My understanding is that this model separately
> estimates variance components for each level of x2 across groups, and does
> not model any correlation between them.
>
> This was a better fit to the data than the structure:
> y ~ x1 + x2 + (x2 | group) ;
> and I came to this model based on a series of threads on this list. Note
> that under this model the correlation between random effects for x2 and the
> intercept was .67, and as far as I can tell convergence was not a problem
> in either model as it might be in some cases with smaller group numbers.
>
> However, I would like to interpret, at least tentatively, the random
> effects, and especially the relationship between them. My central
> substantive question is whether groups vary with respect to differential
> effectiveness with x2 levels (e.g., some groups were effective with x2=0
> but not x2=1 while others were highly effective with both). Extracting the
> random effects and plotting them suggests that even though the model does
> not explicitly include correlations, the two random effects are correlated
> at about r = .56.
>
> My questions are these:
> a) is a significant correlation like r = .56 common under conditions of my
> model in which these effects were not modeled?
> b) to interpret the random effects, I think I may need to treat them as
> additive and correlate u1 with (u1 + u2), which leads to an even higher
> correlation (r > .8). Am I correct in this? My thinking is that u2, as a
> dummy coded variable, represents the deviation for x2 = 1 from x2 = 0, but
> is that incorrect?
>
> Thanks very much,
> Andrew
>
> --
> Andrew McAleavey, M.S.
> Department of Psychology
> The Pennsylvania State University
> 346 Moore Building
> University Park, PA 16802
> aam239 at psu.edu
>
> [[alternative HTML version deleted]]
>
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--
*Ken Beath*
Lecturer
Statistics Department
MACQUARIE UNIVERSITY NSW 2109, Australia
Phone: +61 (0)2 9850 8516
Building E4A, room 526
http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/
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