[R] Glmnet Logistic Variable Questions

[julien giami]

The reason i use glmnet is that it makes the handling of 400,000
observations easier to handle in terms of memory,

I am looking on sparse matrices but i dont understand how to build
interacting using sparse matrices



On Tue, Oct 25, 2011 at 12:34 PM, Marc Schwartz <marc_schwartz at me.com> wrote:
>
> On Oct 25, 2011, at 11:16 AM, Ben Bolker wrote:
>
>> Bert Gunter <gunter.berton <at> gene.com> writes:
>>
>>>
>>> If I understand you correctly, it sounds like you need to do some reading.
>>>
>>> ?lm and ?formula tell you how to specify linear models for glm or glmnet.
>>> However, if you do not have sufficient statistical background, It probably
>>> will be incomprehensible, in which case you should consult your local
>>> statistician.
>>>
>>> For glmnet, go to the linked references given in the Help file.There is no
>>> such thing as AIC for these models, as they are penalized fits (with users
>>> choosing the penalization tradeoff). Again, consult your local statistician
>>
>>  Let me second Bert's concern, but in the meantime, if what you
>> want are *all two-way interactions among variables, you can follow
>> this example:
>>
>>> d <- data.frame(y=runif(100),x1=runif(100),x2=runif(100),x3=runif(100))
>>> gg <- lm(y~(.)^2,data=d)
>>> names(coef(gg))
>> [1] "(Intercept)" "x1"          "x2"          "x3"          "x1:x2"
>> [6] "x1:x3"       "x2:x3"
>>
>>
>> I have done the example with continuous variables and with lm() here,
>> but it should generalize easily to (1) a mixture of categorical and
>> continuous variables and (2) other R modeling functions
>
>
>
> There is a difference with glmnet however vis-à-vis its handling of factors. There is a recent discussion here:
>
>  https://stat.ethz.ch/pipermail/r-help/2011-August/285905.html
>
> which covers the topic. Be sure to read the replies, including Martin's.
>
> HTH,
>
> Marc Schwartz
>
>