Lots of love
Helen.
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> From: Brian Gaines <gaines@cpsc.ucalgary.ca>
> To: pcp@mailbase.ac.uk
> Subject: Re: Principal Components and Repeated Measures
> Date: Friday, January 08, 1999 9:41 PM
>
> Peter, since your replicated variables are still variables it would be
> appropriate to regard your data as a 100 samples of 12 (3x4) variables
and
> carry out PCA in 12 dimensions.
>
> If your replications are expected to result in similar values then you
> would expect to have each set of 4 highly correlated so that the
dimesions
> will appear as closely aligned.
>
> The PCA analysis is 'valid' in the sense that all the variables are
> replicated the same number of times so they will have the same weight in
> the analysis (if your replications gave identical results it would make
no
> difference to the components found how many times they were replicated).
>
> You do not say anything about what the data represents or what you want
to
> get out of the analysis so it is difficult to comment further. Hopefully,
> the above answers the question you posed.
>
> It is important to remember with PCA that what you are doing is rotating
> your data in n-dimensional space in such a way as to spread it out
> maximally. It is a convenient visual way of plotting the data and showing
> correlations.
>
> b.
>
> >I have a question. Assume you have a dataset nxp with multiple
observations
> >from the same subject. So if I have 3 variables on 100 subjects
measured 4
> >times, then my data matrix is 400x3. Does principal components assume
that
> >each row is an independent observation? If I run a PC on the data set
will
> >the principal components still be valid. I would greatly appreciate any
> >help you may offer in this matter. Thanks.
> >
> >
> >
> >PETER L. BONATE, PhD.
> >
> >Quintiles
> >POB 9627 (F4-M3112)
> >Kansas City, MO 64134
> >phone: 816-966-3723
> >fax: 816-966-6999
> >
> >
>
>
>
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