[R] How to make 'step' faster?

[sambit rath]

Thank you Gavin!
I am aware of the lasso regularization routine. But, in this case, my
brief was to perform a stepwise AIC procedure. I guess, subsetting the
data and cross validating it over the rest of the data is the only
answer.

sambit

On 20 August 2010 14:43, Gavin Simpson <gavin.simpson at ucl.ac.uk> wrote:
> On Fri, 2010-08-20 at 12:25 +0530, sambit rath wrote:
>> Dear all,
>>
>> I am fairly new to R. I would like to perform a step-wise logit
>> regression aiming to select a model on the basis of AIC. I am using
>> some large datasets (up to a million rows and 97 variables). It is
>> taking the 'step' function just too long to  complete a single
>> routine. Now, I have tried subsetting the data and perform the same
>> thing. But, 'step' is time consuming still.
>> Can there be a way out?
>
> Rethink your model selection procedure. Look at ridge regression and the
> lasso and elastic net procedures (See the Machine Learning task view on
> CRAN: http://CRAN.R-project.org/view=MachineLearning )
>
> Do you need all million rows? What do they gain you over using a
> smaller, randomly selected subset? You model fitted to the subset can be
> confirmed against the cases omitted from fitting.
>
>> Also, the datasets I am working with contain very few non-zero
>> entries. Can a sparse function specification be used on step?
>
> I don't think this is possible at the moment in R, but several people,
> including Doug Bates and Martin Maechler, are working on bringing sparse
> model matrices and fitting code into R. Doug and Martin's efforts are in
> the unreleased MatrixModels package on R-Forge:
>
> https://r-forge.r-project.org/R/?group_id=61
>
> but it is in active development at a beta stage and doesn't contain any
> stepwise procedures either. The latter isn't a problem as you probably
> want to use a shrinkage method as mentioned above...
>
> HTH
>
> G
>
>>
>> Thank you.
>>
>> Sambit
>>
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>
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