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Titulo: RRegrs: An R package for Computer-aided Model Selection with Multiple Regression Models
Tipo: revista internacional
Fecha: 9,2015
Revista: Journal of Cheminformatics
JCR Journal; Impact Factor: 4.547
Volumen: 7 (46)
Paginas: 1-16
ISSN: 1758-2946


Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others.

    .: SABIA :.  Sistemas Adaptativos y Bioinspirados en Inteligencia Artificial