[R] Package for penalized multivariate Regression
bgunter@4567 @end|ng |rom gm@||@com
Mon Apr 29 18:13:51 CEST 2019
I should have added: for multivariate gam models see e.g. ?mvn
On Mon, Apr 29, 2019 at 7:51 AM Bert Gunter <bgunter.4567 using gmail.com> wrote:
> "but this package do not support multivariate regression."
> "Fits a generalized additive model (GAM) to data, the term ‘GAM’ being
> taken to include any quadratically penalized GLM and a variety of other
> models estimated by a quadratically penalised likelihood type approach (see
> family.mgcv <http://127.0.0.1:24757/help/library/mgcv/help/family.mgcv>).
> So quadratic penalties only.
> Also: This list is about R programming. Questions about statistical
> methodology are generally off topic. Try stats.stackexchange.com instead
> for that.
> 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 Mon, Apr 29, 2019 at 5:07 AM Dominik Schmidt <
> schmidtdominik22 using gmail.com> wrote:
>> Dear all,
>> I want to do a multivariate regression. So I have Y which is a matrix and
>> one vector x which is my predictor variable. I want to do a multivariate
>> regression with penalizing the coefficients I get. Something like:
>> + \lambda b^t P b $ But I have a "own" penalty term which I want to use
>> penalized regression.
>> When I searched for penalized regression, I found a lot of packages but
>> of them have predefined penalties like lasso, ridge or second differences.
>> I used the gam() package in mgcv when I had an univariate response:
>> gam(Y~x_1, paraPen = penaltymatrix)
>> but this package do not support multivariate regression.
>> Is there any R package where I can use an individual penalty matrix to my
>> coefficients? Or can you give me any advice how I solve the problem I
>> Kind regards,
>> [[alternative HTML version deleted]]
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