On Sat, 21 Feb 2009, Tal Galili wrote:
Hello dear R mailing list members.
I have recently became curious of the possibility applying model
selection algorithms (even as simple as AIC) to regressions of large
datasets.
Large in the sense of many observations, one assumes.
But how large in terms of the number of variables??
If not too many variables, then you can form the regression sums of
squares for all 2^p combinations of regressors from a biglm() fit of all
variables as biglm provides coef() and vcov() methods.
If it is large, then you most likely will need to do subsampling to reduce
the number to 'not too many' via lm() and friends then and apply the above
strategy.
I searched as best as I could, but couldn't find any
reference or wrapper for using step or stepAIC to packages such as
biglm.
Surely any direct implementation of step() would be hopelessly long in
execution time.
HTH,
Chuck
Any ideas or directions of how to implement such a concept ?
Best,
Tal
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