HI all,

I might add some more information in order to possibly solve my problem. I'm
really stuck and no obvious solutions do the trick.
I'm using R 1.7.1 on Windows 2000 with the packages regurarly updated.
I'm using hypothetical data constructed as a pseudo population conforming to
a certain Var-Cov structure.
I might add that just

> predict(level2)

works. But when I add the new dataset it doesn't. Following a suggestion I
even tried refactoring of the grouping variable (inter) after I created the
subset. It didn't work. I have no other factor variables in the model. I
really have got no clue what could be wrong.

There is a sample from my data:
> dnNew
Grouped Data: y ~ v11 + v21 + v22 + v23 | inter
         v11             v21          v22         v23    inter
4 5.55186635 5.6620022 24.18033 5.003409 1
13 2.03852426 5.6620022 24.18033 5.003409 1
15 2.19825772 7.5676798 31.03986 4.746891 2
16 4.51368278 7.5676798 31.03986 4.746891 2
18 3.35322702 7.5676798 31.03986 4.746891 2
19 2.46414346 7.5676798 31.03986 4.746891 2
20 2.66670834 7.5676798 31.03986 4.746891 2

and this is the model:
> level2
Linear mixed-effects model fit by REML
Data: d.n.gr.2
Log-restricted-likelihood: -533.0011
Fixed: model$fixed
(Intercept) v11 v21 v22 v23 v11:v21
3.205519074 0.298941539 -0.017743958 0.016007280 -0.410760471 0.002700954
v11:v22 v11:v23
-0.003680952 -0.018005717
Random effects:
Formula: ~v11 | inter
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 0.385620605 (Intr)
v11 0.003147431 -0.048
Residual 0.450012367
Number of Observations: 729
Number of Groups: 50
If this give you some more insight to my problem.

I would reallly appreciate any suggestion.

Thanks

Andrej

-----Original Message-----
From: Andrej Kveder [mailto:andrejk@zrc-sazu.si]
Sent: Monday, September 29, 2003 7:05 PM
To: R-Help
Subject: predicting values from the LME


Dear listers,

I experinced a problem prdicting the values using the LME with multilevel
data.
I have NA's in my dependent variable and the model is fitted only on the
completed cases.
I want to estimate the predicted values for the rest of the data (those
cases with missing dep. variable)
I extracted a subset from the original file containing the variables used in
the model as well as the second level indicator.
I used the following command

p<-predict(level2,newdata=d.n.new,level=0:1)

where level2 is my LME model.
But, I get the following error:

Error in eval(expr, envir, enclos) : 1 argument passed to "$" which requires
2.

I tried with omitting the level specification (which is 0 by default) and I
transformed the new data to be groupedData with no luck.

I have tried the example from the Pinheiro,Bates book and it works - mine
doesn't. Does anybody have an idea what could be wrong?

Thanks for all the suggestions.

Andrej

_________
Andrej Kveder, M.A.
researcher
Institute of Medical Sciences SRS SASA; Novi trg 2, SI-1000 Ljubljana,
Slovenia
phone: +386 1 47 06 440   fax: +386 1 42 61 493

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