You can have look to
*S. Dray*. On the number of principal components: A test of
dimensionality based on measurements of similarity between matrices.
/Computational Statistics and Data Analysis/, 52:2228-2237, 2008.
which is implemented in the testdim function of the ade4 package.
Cheers.
Corrado wrote:
Dear R gurus,
I have some climatic data for a region of the world. They are monthly averages
1950 -2000 of precipitation (12 months), minimum temperature (12 months),
maximum temperature (12 months). I have scaled them to 2 km x 2km cells, and
I have around 75,000 cells.
I need to feed them into a statistical model as co-variates, to use them to
predict a response variable.
The climatic data are obviously correlated: precipitation for January is
correlated to precipitation for February and so on .... even precipitation
and temperature are heavily correlated. I did some correlation analysis and
they are all strongly correlated.
I though of running PCA on them, in order to reduce the number of co-variates
I feed into the model.
I run the PCA using prcomp, quite successfully. Now I need to use a criteria
to select the right number of PC. (that is: is it 1,2,3,4?)
What criteria would you suggest?
At the moment, I am using a criteria based on threshold, but that is highly
subjective, even if there are some rules of thumb (Jolliffe,Principal
Component Analysis, II Edition, Springer Verlag,2002).
Could you suggest something more rigorous?
By the way, do you think I would have been better off by using something
different from PCA?
Best,
--
Stéphane DRAY ([EMAIL PROTECTED] )
Laboratoire BBE-CNRS-UMR-5558, Univ. C. Bernard - Lyon I
43, Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France
Tel: 33 4 72 43 27 57 Fax: 33 4 72 43 13 88
http://biomserv.univ-lyon1.fr/~dray/
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