[R] Package for penalized multivariate Regression
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 <http://127.0.0.1:24757/help/library/mgcv/help/family.mgcv>). "
So quadratic penalties only.
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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:
> + \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,
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