Atropin75 at t-online.de (Felix Eschenburg) writes:
> Dear list,
> i am a student of psychology and have to do a multilevelanalysis on some
data.
>
> About that i have one general and one specific question.
>
> This is what i have copied from the help-file on lme:
> data(bdf)
> fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf,
> random = ~ IQ.ver.cen | schoolNR)
> summary(fm)
>
> after summary(fm) i get the following error:
> Error in verbose || attr(x, "verbose") : invalid `y' type in
`x || y'
Using
Package: nlme
Version: 3.1-48
Date: 2004/01/14
Priority: recommended
Title: Linear and nonlinear mixed effects models
Author: Jose Pinheiro <Jose.Pinheiro at pharma.novartis.com>, Douglas
Bates <bates at stat.wisc.edu>, Saikat DebRoy
<saikat at stat.wisc.edu>, and Deepayan Sarkar
<deepayan at stat.wisc.edu>
Maintainer: Douglas Bates <bates at stat.wisc.edu>
I get
> library(nlme)
> data(bdf)
> fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf,
random = ~ IQ.ver.cen | schoolNR)> summary(fm)
Linear mixed-effects model fit by REML
Data: bdf
AIC BIC logLik
15231.88 15272.01 -7608.938
Random effects:
Formula: ~IQ.ver.cen | schoolNR
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 2.8400175 (Intr)
IQ.ver.cen 0.4604824 -0.635
Residual 6.4295396
Fixed effects: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen
Value Std.Error DF t-value p-value
(Intercept) 40.75032 0.2879720 2155 141.50793 0
IQ.ver.cen 2.46037 0.0838685 2155 29.33607 0
avg.IQ.ver.cen 1.41174 0.3238119 129 4.35976 0
Correlation:
(Intr) IQ.vr.
IQ.ver.cen -0.273
avg.IQ.ver.cen 0.029 -0.212
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-4.17445076 -0.63852405 0.06580449 0.70386427 2.71187810
Number of Observations: 2287
Number of Groups: 131
> I assume that i have not installed the package nlme correcty, but
> reinstalling did not fix that error. So, what can i do about that ?
The nlme package is a required package and should be installed with
any binary version of R. You should not need to install that package
separately.
> Then i have another question about my data.
> I have one response variable and about 20 explanatory variables. These
> variables are nested in a grouping variable of about 100 groups, which is
> nested in another grouping variable of 2 groups .
> I have tried this
> lme.model <- lme(respVar~expVar1, data=myData, random = respVar1+...
> +respVar20| groupingVariable_level2,na.action=na.omit)
I don't think you could have used that syntax. You would have needed
something like
random = ~ respVar1+...+respVar20| groupingVariable_level2
and that is almost certainly not what you want to try to fit. (BTW, I
think you have written respVar where you meant expVar.) That model
has at least 20*(20+1)/2 = 210 variances and covariances to estimate.
It is unlikely that you could do that or that you would want that.
> As i understood Snijders and Bosker, with that i have a fixed effect of
> expVar1 on respVar and a random effect of all the others explanatory
> variables. They are also nested in grouping Variable level 2.
> Now my question is, if this is the correct term and how do i include the
> groupingVariable 3 into my model ?
I would start with simpler models and take a lot at a lot of
diagnostic plots along the way.
> As you can see, i just begun to learn about multilevelanalysis, so
> if you have a link, where it is explained, how to do that with R, I
> would be very thankful.
Well there is a book by Pinheiro and Bates (Springer, 2000) called
"Mixed-effects models in S and S-PLUS" that you may want to look at.