Re: [R] Bayesian PCA

From: Christian Hennig <chrish_at_stats.ucl.ac.uk>
Date: Tue, 12 Apr 2011 16:38:00 +0100 (BST)

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 questions. 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 possible.
You can then use PCA in order to examine the remaining dimensions

Christian

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,
>
> Lucy
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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 12 Apr 2011 - 15:42:14 GMT

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