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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Christian Hennig
University College London, Department of Statistical Science
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