Re: Principal Components and Repeated Measures

Helen Jones (
Sat, 9 Jan 1999 11:13:35 -0800

Thanks Sumitra, Andy, Sita and Liam - what a nice surprise! Just picked
it up before going to lunch with B, P, J&M"! Hope you had a good holiday.
I am glad you got my card - no one in England did so I thought my system
was not working.

Lots of love


> From: Brian Gaines <>
> To:
> 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
> 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
> 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
> 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
> 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
> >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
> >each row is an independent observation? If I run a PC on the data set
> >the principal components still be valid. I would greatly appreciate any
> >help you may offer in this matter. Thanks.
> >
> >
> >
> >
> >Quintiles
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> >
> >