R help - Nov 2008 - 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


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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


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View this message in context:
http://www.nabble.com/lmer-p-values-for-fixed-effects-missing-tp20569116p20569116.html
Sent from the R help mailing list archive at Nabble.com.

> -----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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