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Open Access Publications from the University of California

Software for prediction and estimation with applications to high-dimensional genomic and epidemiologic data

  • Author(s): Ritter, Stephan Johannes
  • Advisor(s): Hubbard, Alan E.
  • et al.

Three add-on packages for the R statistical programming environment (R Core Team, 2013) are described, with simulations demonstrating performance gains and applications to real data. Chapter 1 describes the relaxnet package, which extends the glmnet package with relaxation (as in the relaxed lasso of Meinshausen, 2007). Chapter 2 describes the widenet package, which extends relaxnet with polynomial basis expansions. Chapter 3 describes the multiPIM package, which takes a causal inference approach to variable importance analysis. Section 3.7 describes an analysis of data from the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study (Rahbar et al., 2012; Hubbard et al., 2013), for which the multiPIM package is used in conjunction with the relaxnet and widenet packages to estimate variable importances.

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