> -----Original Message-----
> From: r-help-bounces at r-project.org on behalf of UKaraoz
> Sent: Tue 11/18/2008 2:16 PM
> To: r-help at r-project.org
> Subject: [R] lmer p-values for fixed effects missing
>
>
> I am trying to replicate the repeated measures example from
Dr.Faraway's book
> (Extending the linear model with R) as follows:
>
> data(vision)
> vision$npower <- rep(1:4,14)
>
> mmod <-lmer(acuity~power+(1|subject)+(1|subject:eye),vision)
>
>
> When I look at the fixed effects p-value, it is missing. Am I missing
> something here?
> Thanks.
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 112.6429 2.2348 50.40
> power6/18 0.7857 1.5400 0.51
> power6/36 -1.0000 1.5400 -0.65
> power6/60 3.2857 1.5400 2.13
>
See the discussion titled "lmer, p-values and all that" at
https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html
and a collection of related discussions at
http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests
In a nutshell:
The complexity of fully crossed mixed effects models
makes degrees of freedom and other model attributes
more difficult to calculate compared to fixed effects
models.
Research and debate is ongoing as to the best way to
handle model assessment, not all classical summary
statistics and associated p-values map to lmer models
in a straightforward fashion.
Steven McKinney
Statistician
Molecular Oncology and Breast Cancer Program
British Columbia Cancer Research Centre
email: smckinney +at+ bccrc +dot+ ca
tel: 604-675-8000 x7561
BCCRC
Molecular Oncology
675 West 10th Ave, Floor 4
Vancouver B.C.
V5Z 1L3
Canada
>
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