I commend you to (a) the recent article by Doug Bates on "Fitting
nonlinear mixed models in R" pp. 27-30 in the latest issue of "R
News"
available from "www.r-project.org" -> Newsletter and (b) Doug's
book
with Pinheiro (2000) Mixed-Effects Models in S and S-PLUS (Springer). I
suggest you try the same analysis using in "lmer", library(lme4), and
"lme", library(nlme), with method = "ML", as explained in
Pinheiro and
Bates. If you have trouble with this, please post another question on
this, preferably using either a standard data set distributed with R or
one of the standard packages or a very simple made-up data set with very
few observations that you can distribute with your question in a short
sequence of R commands illustrating something you tried that either
didn't work or that gave results you don't understand. I can't do
much
more with the example you've provided below, because I don't know how to
access the your data. (And PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html if you haven't already.)
hope this helps.
spencer graves
RenE J.V. Bertin wrote:
> Hello,
>
> I'm trying to understand how to interpret the differences in results
between two versions of a 2-factor ANOVA with (slightly?) different models, of
an observable y, a within-subject factor 'indep' and a grouping factor
'cond' (and a subject 'factor' Snr):
>
>
>>summary( aov( y~cond + indep + Error(Snr/indep) ) )
>
> # example results:
> Error: Snr
> Df Sum Sq Mean Sq F value Pr(>F)
> cond 1 103.1 103.1 1.425 0.248
> indep 5 159.8 32.0 0.442 0.813
> Residuals 18 1301.6 72.3
>
> Error: Snr:indep
> Df Sum Sq Mean Sq F value Pr(>F)
> indep 5 20.81 4.16 3.167 0.0104 *
> Residuals 111 145.89 1.31
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
>
> Error: Within
> Df Sum Sq Mean Sq F value Pr(>F)
> Residuals 137 22.178 0.162
>
>
>>summary( aov( y~cond * indep + Error(Snr/indep) ) )
>
> # example results:
> Error: Snr
> Df Sum Sq Mean Sq F value Pr(>F)
> cond 1 174.6 174.6 1.689 0.213
> indep 5 201.9 40.4 0.391 0.848
> cond:indep 5 124.0 24.8 0.240 0.939
> Residuals 15 1550.8 103.4
>
> Error: Snr:indep
> Df Sum Sq Mean Sq F value Pr(>F)
> indep 5 73.16 14.63 8.601 5e-07 ***
> cond:indep 5 21.32 4.26 2.507 0.0336 *
> Residuals 125 212.64 1.70
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
>
> Error: Within
> Df Sum Sq Mean Sq F value Pr(>F)
> Residuals 464 507.5 1.1
>
>
> I would like to understand a bit better what the cond:indep line under the
second Error:Snr:indep can mean. If I understood correctly, this represents some
"higher-order" interaction, but not a real indep/cond interaction.
What I also do not grasp is why the indep effect's F and significance is so
different between the two models.
> Finally, what does it mean when significant effects are listed under the
Error:Within line?
>
> Is there a good resource available (web, or if not printed) which discusses
this kind of question in a way accessible to non statisticians? The last time I
checked, manuals like "R for Psychologists" do not really enter into
this level of detail...
>
> Thanks very much in advance,
> R. Bertin
>
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