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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