Thanks! The reason would be my ignorance not yours!
I found some SAS code a year or so back for using Proc Mixed for some
similar questions and adapted it to my purposes in R. Your approach is
certainly a lot smoother and cumbersome. Your "obs" recommendation also
looks good, I'll play around with that and compare it to MCMCglmm's within
individual estimates.

Thanks a lot,
Ned





Ben Bolker wrote:
 Maybe displaying my ignorance, but is there a particular reason
to use dummy variables instead of a factor?  That is, if I start with

ind   y   z
A    1.2  2.4
A    1.3  2.1
B    2.1  3.2
B    2.3  4.6
...

and then use melt(data,id.var=1) from reshape I get data that look like

ind  variable value
A     y      1.2
A     z      2.4
A     y      1.3
A     z      2.1
....

and then

lmer(value~variable-1+(
variable-1|ind),data=ind.data) ?

it seems you could also add an "obs" variable to the original
data frame, use it as an id var as well, and be able to get a
correlation among observations taken at the same time within
the same individual ...


Ned Dochtermann <ned.dochtermann@...> writes:

>
> All,
>
> I am attempting to "cheat" with lme4 to calculate the correlation between
> two traits--y & z--measured repeatedly (simulation data actually) for
> individuals by use of dummy variables. I can calculate the
> between-individual variances and covariances with the data structured as:
>
> ind   y_rand          z_rand          yz
> A     1               0               -1.941248639
> A     1               0               0.747675528
> B     1               0               2.502358777
> B     1               0               2.752530485

 [snip]

> and then fitting the following model:
>
> yz.mm<-lmer(yz~-1+y_rand+z_
rand+(y_rand+z_rand-1|ind),data=ind.data)
>
> This gives seemingly correct results (i.e. consistent with results from
> MCMCglmm and ASReml) for the between-individual estimates. The issue that
> I'm running into is that since I'm treating y and z as the same variable,
I
> can't figure out how to get any sort of within-individual estimates beyond
a
> single variance estimate. This makes sense given the way I've formulated
the
> model.
>
> Is there any way to likewise cheat to get within-individual/group variance
> and covariance estimates with lme4? I can get what I need from MCMCglmm
but,
> for computational reasons I'd prefer lme4.
>
> Thanks for any help,
> Ned
>
> p.s. just realized that I can probably get what I want from lme...
>

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