gms | German Medical Science

MAINZ//2011: 56. GMDS-Jahrestagung und 6. DGEpi-Jahrestagung

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V.
Deutsche Gesellschaft für Epidemiologie e. V.

26. - 29.09.2011 in Mainz

Robust generalized lineal models in R

Meeting Abstract

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  • Justo Lorenzo Bermejo - Universität Heidelberg, Heidelberg
  • Alfonso Garcia Perez - Universidad Nacional de Educacion a Distancia (UNED), Madrid

Mainz//2011. 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz, 26.-29.09.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11gmds067

DOI: 10.3205/11gmds067, URN: urn:nbn:de:0183-11gmds0671

Published: September 20, 2011

© 2011 Lorenzo Bermejo et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

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The generalized linear model (GLM) allows the distribution of the dependent variable to belong to the exponential family, which also includes not continuous distributions. Other than simple linear relationships between response and explanatory variables are permitted. The robust approach to statistical modeling aims at deriving methods that produce reliable estimates not only when data follow a given distribution exactly, but also when this happens approximately. We have explored the current facilities of the free software environment for statistical computing R to identify influential cases in GLMs and to carry out robust GLMs. We summarize relevant theoretical and technical details, and apply R to investigate several real datasets by robust and standard GLMs, including robust variance estimates. Methods and software for robust estimation of GLM are still sparse and mainly limited to Logistic and Poisson regression. The “car” and “robustbase” packages provide convenient functions for diagnostic plots and robust GLMs. In many practical situations, the implementation of robust GLMs is relatively straightforward. During the presentation, we will illustrate the benefit of comparing standard and robust GLM estimates by using real examples.