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" <[EMAIL PROTECTED]> 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 5 318.263.6 8.336 7.46e-07 ***
WasSick:T.SPart 5 125.525.1 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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