Model-based boosting in R: a hands-on tutorial using the R package mboost

Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost

We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboos...

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Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost. pdfsubject

We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboos...

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Benjamin Hofner , Andreas Mayr , Nikolay Robinzonov , Matthias Schmid Model - based Boosting in R : A Hands - on Tutorial Using the R Package mboost

We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboos...

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A Tutorial on Using the R Package FLLat

This document provides a brief tutorial on using the FLLat package, which implements the Fused Lasso Latent Feature (FLLat) model (Nowak and others, 2011). The FLLat model is designed to identify regions of copy number variation (CNV) in multi-sample aCGH data. In particular, it takes advantage of any similarities shared among samples while maintaining the ability to identify any potential hete...

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Meta-Analysis in R Using Package rmeta

Meta-Analysis means a statistical analysis on the results of their findings is to combine a large number of independent studies. Meta-Analysis software is needed to be done for convenience, Statistical Software for Meta-Analysis is available in many statistical packages available in the present study provide software R is for Meta-Analysis. First, a brief description of the Meta-Analysis, Stati...

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Model-based boosting in R: a hands-on tutorial using the R package mboost

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