The betaboost package-a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data
To provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable. The proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R. The betaboost methodology is based o...
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| Published in | International journal of epidemiology Vol. 47; no. 5; pp. 1383 - 1388 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
01.10.2018
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| Online Access | Get full text |
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| Abstract | To provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable.
The proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R.
The betaboost methodology is based on beta-regression, which is a state-of-the-art method for modelling bounded outcome variables. By combining traditional model fitting techniques with recent advances in statistical learning and distributional regression, betaboost allows users to carry out data-driven variable and/or confounder selection in potentially high-dimensional epidemiological data. The software package implements a flexible routine to incorporate linear and non-linear predictor effects in both the mean and the precision parameter (relating inversely to the variance) of a beta-regression model.
The software is hosted publicly at [http://github.com/boost-R/betaboost] and has been published under General Public License (GPL) version 3 or newer. |
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| AbstractList | MotivationTo provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable.ImplementationThe proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R.General featuresThe betaboost methodology is based on beta-regression, which is a state-of-the-art method for modelling bounded outcome variables. By combining traditional model fitting techniques with recent advances in statistical learning and distributional regression, betaboost allows users to carry out data-driven variable and/or confounder selection in potentially high-dimensional epidemiological data. The software package implements a flexible routine to incorporate linear and non-linear predictor effects in both the mean and the precision parameter (relating inversely to the variance) of a beta-regression model.AvailabilityThe software is hosted publicly at [http://github.com/boost-R/betaboost] and has been published under General Public License (GPL) version 3 or newer. To provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable. The proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R. The betaboost methodology is based on beta-regression, which is a state-of-the-art method for modelling bounded outcome variables. By combining traditional model fitting techniques with recent advances in statistical learning and distributional regression, betaboost allows users to carry out data-driven variable and/or confounder selection in potentially high-dimensional epidemiological data. The software package implements a flexible routine to incorporate linear and non-linear predictor effects in both the mean and the precision parameter (relating inversely to the variance) of a beta-regression model. The software is hosted publicly at [http://github.com/boost-R/betaboost] and has been published under General Public License (GPL) version 3 or newer. |
| Author | Weinhold, Leonie Hofner, Benjamin Gefeller, Olaf Mayr, Andreas Titze, Stephanie Schmid, Matthias |
| Author_xml | – sequence: 1 givenname: Andreas surname: Mayr fullname: Mayr, Andreas organization: Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany – sequence: 2 givenname: Leonie surname: Weinhold fullname: Weinhold, Leonie organization: Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany – sequence: 3 givenname: Benjamin surname: Hofner fullname: Hofner, Benjamin organization: Section Biostatistics, Paul-Ehrlich-Institut, Langen, Germany – sequence: 4 givenname: Stephanie surname: Titze fullname: Titze, Stephanie organization: Department of Nephrology and Hypertension, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany – sequence: 5 givenname: Olaf surname: Gefeller fullname: Gefeller, Olaf organization: Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany – sequence: 6 givenname: Matthias surname: Schmid fullname: Schmid, Matthias organization: Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany |
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