R help - Aug 2002 - [nlme] BLUPs for a new subject in a fitted lme model?

I am seeking for a method to calculate, given a fitted lme model
and some data for a subject, the random effects predictors
for this subject. I can only find predictors for the subjects used in
creating the fit. Of course I could just add the subject and redo the fit.
But I want to avoid just this refitting. 

Thanks for help
wbk
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On Fri, 16 Aug 2002, Wilhelm B. Kloke wrote:
> I am seeking for a method to calculate, given a fitted lme model
> and some data for a subject, the random effects predictors
> for this subject. I can only find predictors for the subjects used in
> creating the fit. Of course I could just add the subject and redo the fit.
> But I want to avoid just this refitting. 
> 
> Thanks for help
> wbk
Once you've a lme model, you don't need to stimate new random effects
for
new subjects.

As stated by Pinheiro & Bates:
"Fixed affects are parameters associated with an entire population, or 
with repeatable levels of experimental factors.
Random effects are instead associated with experimental units drawn at 
random from a population."

Maybe you'd like to see Pinheiro & Bates' book for further details.


-- 
Ulises M. Alvarez
LAB. DE CHOQUES DEBILES
FISICA APLICADA Y TECNOLOGIA AVANZADA
UNAM
umalvarez at fata.unam.mx


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In article <ifado.list.r.help/Pine.LNX.4.31.0208191041401.13359-100000 at
gannet.stats>,
 <ripley at stats.ox.ac.uk> wrote:
>On Mon, 19 Aug 2002 iwhite at staffmail.ed.ac.uk wrote:
>
>> In the simple model Y = mu + U + e, where U and e have variances Vu and
>> Ve, the BLUP for a subject with observation Y is
>>
>> E(U|Y) = Vu  * (Y - mu)
>>         ----
>>        (Vu+Ve)
>>
>> Similarly for more complicated models, i.e. the BLUP is a simple
function
>> of the variance components. I'm not sure whether this answers the
original
>> question, but it must be relevant and nobody has mentioned it so far.
>
>Yes, it is relevant.
>
>In general the BLUP is not a simple function like that, not with
>hierarchical designs and e.g. random effects on slopes as well as
>intercepts.  Which is why I'd like my software to do it for me.
Let me sharpen my original question. Given new observations (on a new
or even an old subject), is there a simple test whether these new
observations are fitted by the model? As I understand it, if I had the BLUPs
I could just compare the residuals of the fit with the model's residuals
and those of a fixed effects model only for the new data.

wbk
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