[R] FW: error predicting values from the LME

Spencer Graves spencer.graves at pdf.com
Tue Sep 30 15:13:13 CEST 2003

      Could you dumb it down to a toy example with 4 observations for a 
model like "y ~ 1 | inter", 2 observations for each of 2 levels of 
"inter"?  If that works, then you can play with the example that works 
and the example that doesn't;  this is one of the strategies mentioned 
in Poly (1971) How to Solve It (Princeton U. Pr.).  With luck, this will 
help you figure out what you need to do to get the answers you want.  If 
not, it should help you produce a small toy example that doesn't work, 
which you can then send us.  Please include a data.frame call, so 
someone can copy your example into R and try it in 2 seconds.  That 
should increase the chances that you would get a helpful reply. 

      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
>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:
>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:
>Linear mixed-effects model fit by REML
>Data: d.n.gr.2
>Log-restricted-likelihood: -533.0011
>Fixed: model$fixed
>(Intercept) v11 v21 v22 v23 v11:v21
>3.205519074 0.298941539 -0.017743958 0.016007280 -0.410760471 0.002700954
>v11:v22 v11:v23
>-0.003680952 -0.018005717
>Random effects:
>Formula: ~v11 | inter
>Structure: General positive-definite, Log-Cholesky parametrization
>StdDev Corr
>(Intercept) 0.385620605 (Intr)
>v11 0.003147431 -0.048
>Residual 0.450012367
>Number of Observations: 729
>Number of Groups: 50
>If this give you some more insight to my problem.
>I would reallly appreciate any suggestion.
>-----Original Message-----
>From: Andrej Kveder [mailto:andrejk at zrc-sazu.si]
>Sent: Monday, September 29, 2003 7:05 PM
>To: R-Help
>Subject: predicting values from the LME
>Dear listers,
>I experinced a problem prdicting the values using the LME with multilevel
>I have NA's in my dependent variable and the model is fitted only on the
>completed cases.
>I want to estimate the predicted values for the rest of the data (those
>cases with missing dep. variable)
>I extracted a subset from the original file containing the variables used in
>the model as well as the second level indicator.
>I used the following command
>where level2 is my LME model.
>But, I get the following error:
>Error in eval(expr, envir, enclos) : 1 argument passed to "$" which requires
>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 Kveder, M.A.
>Institute of Medical Sciences SRS SASA; Novi trg 2, SI-1000 Ljubljana,
>phone: +386 1 47 06 440   fax: +386 1 42 61 493
>	[[alternative HTML version deleted]]
>R-help at stat.math.ethz.ch mailing list

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