Combines regularized regression methods with the estimation of Graphical Gaussian models. parcor framework includes various existing methods as well as two new approaches based on ridge regression and adaptive lasso, respectively. It estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, Partial Least Squares (PLS), and Ridge Regression. These methods are extensively compared both qualitatively and quantitatively within a simulation study and through an application to six diverse real data sets.

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parcor specifications

Software type:
Framework
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 3.0, GNU General Public License version 2.0
Version:
2.2-6
Requirements:
MASS, glmnet, ppls, Epi, GeneNet
Source code URL:
https://cran.r-project.org/src/contrib/parcor_0.2-6.tar.gz

Publications

  • (Krämer et al., 2009) Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics.
    PMID: 19930695

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Documentation

Credits

Institution(s)

Machine Learning/Intelligent Data Analysis Group, Berlin Institute of Technology, Berlin, Germany; Seminar für Statistik, ETH Zurich, Zurich, Switzerland; Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland; Department of Statistics, University of Munich, Munich, Germany; Computational Molecular Medicine Research Group, Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany

Funding source(s)

This work was supported by the BMBF grant FKZ 01-IS07007A (ReMind), the FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence, ICT-216886, by DSM Nutritional Products Ltd. (Basel, Switzerland) and by the LMU-innovativ Project BioMed-S: Analysis and Modelling of Complex Systems in Biology and Medicine.

Link to literature

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