Also, have you read Pinhiero and Bates (2000) Mixed-Effect Models in S and S-Plus (Springer)? I've found that book to be indispensible for using "lme".
hope this helps. spencer graves
Andrej Kveder wrote:
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$fixed (Intercept) v11 v21 v22 v23 v11:v21 3.205519074 0.298941539 -0.017743958 0.016007280 -0.410760471 0.002700954 v11:v22 v11:v23 -0.003680952 -0.018005717 Random effects: Formula: ~v11 | inter Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 0.385620605 (Intr) v11 0.003147431 -0.048 Residual 0.450012367 Number of Observations: 729 Number of Groups: 50 If this give you some more insight to my problem.
I would reallly appreciate any suggestion.
Thanks
Andrej
-----Original Message----- From: Andrej Kveder [mailto:[EMAIL PROTECTED] Sent: Monday, September 29, 2003 7:05 PM To: R-Help Subject: predicting values from the LME
Dear listers,
I experinced a problem prdicting the values using the LME with multilevel data. I have NA's in my dependent variable and the model is fitted only on the completed cases. I want to estimate the predicted values for the rest of the data (those cases with missing dep. variable) I extracted a subset from the original file containing the variables used in the model as well as the second level indicator. I used the following command
p<-predict(level2,newdata=d.n.new,level=0:1)
where level2 is my LME model. But, I get the following error:
Error in eval(expr, envir, enclos) : 1 argument passed to "$" 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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