R help - Apr 2011 - Bayesian PCA

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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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
> This message and any attachment are intended solely for the addressee and
may contain confidential information. If you have received this message in
error, please send it back to me, and immediately delete it.   Please do not
use, copy or disclose the information contained in this message or in any
attachment.  Any views or opinions expressed by the author of this email do not
necessarily reflect the views of the University of Nottingham.
>
> This message has been checked for viruses but the contents of an attachment
> may still contain software viruses which could damage your computer system:
> you are advised to perform your own checks. Email communications with the
> University of Nottingham may be monitored as permitted by UK legislation.
> 	[[alternative HTML version deleted]]
>
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche