[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."

"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 <>). "

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>

> 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
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