Re: [R] glmnet vignette question

[Bert Gunter]

You seem to be mainly asking for help with statistical methodology,
which is generally off topic for this list, which is about help with R
programming. I suggest you study the references given in the
vignette/package and/or post to a statistical list like
stats.stackexchange.com instead.

Cheers,
Bert
Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Fri, Sep 16, 2016 at 9:33 AM, Dominik Schneider
 wrote:
>> Is there a way to extract MSE for a lambda, e.g. lambda.1se?
> nevermind this specific question. it's now obvious. However my overall
> question stands.
>
> On Fri, Sep 16, 2016 at 10:10 AM, Dominik Schneider <
> dominik.schnei...@colorado.edu> wrote:
>
>> I'm doing some linear modeling and am new to the ridge/lasso/elasticnet
>> procedures. In my case I have N>>p (p=15 based on variables used in past
>> literature and some physical reasoning) so my understanding is that I
>> should be interested in ridge regression to avoid the issue of
>> multicollinearity of predictors.  Lasso is useful when p>>N.
>>
>> In the past I have performed step-wise regression with stepAIC in both
>> directions to choose my variables and then used VIF to determine if any of
>> these variables are correlated. My understanding is that ridge regression
>> is a more robust approach for this workflow.
>>
>> Reading the glmnet_beta vignette, it describes the alpha parameter where
>> alpha=1 is a lasso regression and alpha=0 is a ridge regression. Farther
>> down the authors suggest a 10 fold validation to determine an alpha value
>> and based on the plots shown, say that alpha=1 does the best here. However,
>> all the models look like they approach the same MSE and alpha=0 is the
>> lowest curve for all lambda (but maybe this second point doesn't matter?).
>> With my data I get a very similar looking set of curves so I'm trying to
>> decide if I should stick with alpha=1 instead of alpha=0. Is there a way to
>> extract MSE for a lambda, e.g. lambda.1se?
>>
>> Any advice or clarification is appreciated. Thanks.
>> Dominik
>>
>>
>
> [[alternative HTML version deleted]]
>
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Re: [R] glmnet vignette question

[Dominik Schneider]

> Is there a way to extract MSE for a lambda, e.g. lambda.1se?
nevermind this specific question. it's now obvious. However my overall
question stands.

On Fri, Sep 16, 2016 at 10:10 AM, Dominik Schneider <
dominik.schnei...@colorado.edu> wrote:

> I'm doing some linear modeling and am new to the ridge/lasso/elasticnet
> procedures. In my case I have N>>p (p=15 based on variables used in past
> literature and some physical reasoning) so my understanding is that I
> should be interested in ridge regression to avoid the issue of
> multicollinearity of predictors.  Lasso is useful when p>>N.
>
> In the past I have performed step-wise regression with stepAIC in both
> directions to choose my variables and then used VIF to determine if any of
> these variables are correlated. My understanding is that ridge regression
> is a more robust approach for this workflow.
>
> Reading the glmnet_beta vignette, it describes the alpha parameter where
> alpha=1 is a lasso regression and alpha=0 is a ridge regression. Farther
> down the authors suggest a 10 fold validation to determine an alpha value
> and based on the plots shown, say that alpha=1 does the best here. However,
> all the models look like they approach the same MSE and alpha=0 is the
> lowest curve for all lambda (but maybe this second point doesn't matter?).
> With my data I get a very similar looking set of curves so I'm trying to
> decide if I should stick with alpha=1 instead of alpha=0. Is there a way to
> extract MSE for a lambda, e.g. lambda.1se?
>
> Any advice or clarification is appreciated. Thanks.
> Dominik
>
>

[[alternative HTML version deleted]]

__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.