[R] some help interpreting ANOVA results, please?

[Christoph Scherber]

Dear RenE,

Can you explain a bit more how you derive your T.SPart? That´s what I 
think is the tricky part of your analysis.

I would suggest you should try to end up with something like this:

model1<-aov(SR~WasSick*Time+Error(Subject/Time)
model2<-aov(SR~SC*Time+Error(Subject/Time)

This way it would be like a repeated measures ANOVA, where WasSick (or 
SC) are the primary covariates, and Time is nested within Subject.

I think the correct specification of "time" is crucial for the whole 
analysis. It´s like in a split-plot ANOVA, where finding the appropriate 
codings for plots of different sizes can sometimes take a very long time.

Regards,
Christoph


0) Subject, the subject identifier
1) physiological recordings, say SR (skin resistance): time series
2) a SessionPart variable (parts R1 and R2, separated in time by a pause)
3) time, T.SPart: normalised per subject and per SessionPart, so twice 0..1
4) a subjective sickness estimate (SC): time series
5) a per-subject classification: WasSick or not (available as a time series, but constant in time of course)



 

RenE J.V. Bertin wrote:

>On Sun, 10 Oct 2004 19:55:41 +0200, "RenE J.V. Bertin" <rjvbertin at hotmail.com> wrote regarding "Re:
>[R] some help interpreting ANOVA results, please?"
>
>I'm would like to come back to a question I posted quite a while ago, concerning the analysis of data of an ongoing experiment. I have, for a given number of subjects:
>0) Subject, the subject identifier
>1) physiological recordings, say SR (skin resistance): time series
>2) a SessionPart variable (parts R1 and R2, separated in time by a pause)
>3) time, T.SPart: normalised per subject and per SessionPart, so twice 0..1
>4) a subjective sickness estimate (SC): time series
>5) a per-subject classification: WasSick or not (available as a time series, but constant in time of course)
>
>I would like to make statements on whether or not sickness (measured by 4 or 5) can be deduced from the physiological recordings, e.g. something like
>  
>
>>aov( SR ~ WasSick * T.SPart )
>>    
>>
>
>expecting a significant effect of time (sickness building up), of WasSick, and a significant interaction showing that the effect is stronger (or only significant) in the WasSick=TRUE subjects. A simple t.test(SR~WasSick) gives a significant difference, as well as t.test( SR~ (T.SPart>=0.5) ) .
>
>The problem I'm having is that WasSick (and SC) are not independent variables properly speaking. So I cannot do
>
>  
>
>>aov( SR ~ WasSick * T.SPart + Error(Subject/WasSick*T.SPart) )
>>    
>>
>
>R would remove WasSick from the Error term, and do the analysis without it, giving a significant T.SPart effect and WasSick:T.SPart interaction (?), both listed under Error: Subject:T.SPart :
>Error: Subject:T.SPart
>                            Df Sum Sq Mean Sq F value   Pr(>F)    
>T.SPart                      5  318.2    63.6   8.336 7.46e-07 ***
>WasSick:T.SPart              5  125.5    25.1   3.289   0.0079 ** 
>Residuals                  129  984.9     7.6                     
>
>There is no trace of a WasSick effect other than in that interaction (of which I'm not sure it is truly one).
>
>
>
>I have 2 questions at this point:
>
>A) I think one could assimilate WasSick to a grouping variable (like in a clinical stdudy), forgetting it is actually an observation on the subjects. In that case, I could do
>  
>
>>aov( SR ~ WasSick * T.SPart )
>>    
>>
>which gives me the expected two significant main effects and the significant interaction (which agrees with visual inspection of the data).
>Is this an acceptable approach/model?
>
>B) Should I contine putting the Subject id in an Error term, e.g.
>  
>
>>aov( SR ~ WasSick + Error(Subject) )
>>    
>>
>WithOUT this error term, that anova gives a significant effect, confirming the t.test mentioned above. If I include the error term, the effect is no longer significant.
>Is that because the model does not make sense, rather because my data are so non-normal that a t.test cannot be used? (?Error has a similar model, and calls it "not particularly sensible statistically".)
>
>
>I would really appreciate some more constructive comments!
>Thanks,
>RenE Bertin
>
>PS: I must add that it has been suggested to try lme. I went over what docs I have (help and MASS 4), but these are far to specialistic for me, so I haven't gotten anywhere in that direction :(
>
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