# [R] Package for penalized multivariate Regression

Bert Gunter bgunter@4567 @end|ng |rom gm@||@com
Mon Apr 29 16:51:02 CEST 2019

"but this package do not support multivariate regression."
Wrong.

"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>). "

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:
> $||y-xb|| > + \lambda b^t P b$ But I have a "own" penalty term which I want to use for
> penalized regression.
>
> When I searched for penalized regression, I found a lot of packages but all
> 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 have?
>
> Kind regards,
>
> Dominik
>
>         [[alternative HTML version deleted]]
>
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