Email this article 
Hide
Show all
| Authors: | Benjamin Hofner, Andreas Mayr, Matthias Schmid | ||||
| Title: | gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework | ||||
| Abstract: | Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=gamboostLSS. | ||||
|
Page views:: 2841. Submitted: 2014-07-04. Published: 2016-10-20. |
|||||
| Paper: |
gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework
Download PDF
(Downloads: 2166)
|
||||
| Supplements: |
| ||||
| DOI: |
10.18637/jss.v074.i01
|
This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |