Re: [R] Bayesian PCA

From: William Revelle <>
Date: Tue, 12 Apr 2011 14:48:58 -0500

Dear Lucy,

   You might consider some of the scale construction techniques available in the psych package. In particular, the iclust function is meant for this very problem: how to form reliable item composites.


At 4:38 PM +0100 4/12/11, Christian Hennig wrote:
>Dear Lucy,
>not an R-related response at all, but if it's questionnaire data,
>I'd probably try to do dimension reduction in a non-automated way by
>defining a number of 10 or so meaningful scores that summarise your
>Dimension reduction is essentially about how to aggregate the given
>information into low-dimensional measurements, which according to my
>opinion should be rather driven by the research aim and meaning of
>the variables than by the distribution of the data, if at all
>You can then use PCA in order to examine the remaining dimensions
>On Tue, 12 Apr 2011, Lucy Asher wrote:
>>First of all I should say this email is more of a general
>>statistics questions rather than being specific to using R but I'm
>>hoping that this may be of general interest.
>>I have a dataset that I would really like to use PCA on and have
>>been using the package pcaMethods to examine my data. The results
>>using traditional PCA come out really nicely. The dataset is
>>comprised of a set of questions on dog behaviour answered by their
>>handlers. The questions fall into distinct components which may
>>biological sense and the residuals are reasonable small. Now the
>>problem. I don't have a big enough sample to run traditional PCA. I
>>have about 40 dogs and 60 questions so which ever way you look at
>>it not enough. There is past data available on some of the
>>questions and the realtionships between them so I was wondering
>>whether Bayesian PCA would be a useful alternative using past
>>research to inform my priors. I wondered if anyone knew whether
>>Bayesian PCA was better suited to smaller datasets than traditional
>>(ML) PCA? If not I wondered if anyone knew of packages in R that
>>could do dimension reduction on datasets with small sample sizes?
>>Many Thanks,
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>*** --- ***
>Christian Hennig
>University College London, Department of Statistical Science
>Gower St., London WC1E 6BT, phone +44 207 679 1698
> mailing list
>PLEASE do read the posting guide
>and provide commented, minimal, self-contained, reproducible code.

William Revelle
Department of Psychology   
Northwestern University
Use R for psychology             
It is 6 minutes to midnight

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Received on Tue 12 Apr 2011 - 19:52:37 GMT

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