gamlss.lasso-package: Extra Lasso-Type Additive Terms for GAMLSS
In gamlss.lasso: Extra Lasso-Type Additive Terms for GAMLSS

Description Details Author(s) References See Also Examples

Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression.

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Florian Ziel [aut, cre], Peru Muniain [aut], Mikis Stasinopoulos [ctb]

Maintainer: Florian Ziel <florian.ziel@uni-due.de>

R Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape, (with discussion), Appl. Statist., 54, part 3, pp 507-554.

Rigby R.A., Stasinopoulos D. M., Heller G., and De Bastiani F., (2019) Distributions for Modeling Location, Scale and Shape: Using GAMLSS in R, Chapman and Hall/CRC.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of statistics, 32(2), 407-499.

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.

gamlss, gamlss.family, gamlss.add

# Contructing the data
library(gamlss.lasso)
set.seed(123)
n<- 500
d<- 50
X<- matrix(rnorm(n*d), n,d)
BETA<- cbind( "mu"=rbinom(d,1,.1), "sigma"= rbinom(d,1,.1)*.3)
ysd<- exp(1 + tcrossprod( BETA[,2],X))
data<- cbind(y=as.numeric(rnorm(n, sd=ysd))+t(tcrossprod( BETA[,1],X)), as.data.frame(X))

# Estimating the model with gnet default setting
mod <- gamlss(y~gnet(x.vars=names(data)[-1] ),
              sigma.fo=~gnet(x.vars=names(data)[-1]), data=data, family=NO,
              i.control = glim.control(cyc=1, bf.cyc=1))

# Estimated paramters are available at
rbind(true=BETA[,1],estimate=tail(getSmo(mod, "mu") ,1)[[1]]$beta )## beta for mu
rbind(true=BETA[,2],estimate=tail(getSmo(mod, "sigma") ,1)[[1]]$beta )## beta for sigma