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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