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Software for Prediction and Estimation with Applications to High-Dimensional Genomic and Epidemiologic Data

Posted on:2014-10-22Degree:Ph.DType:Dissertation
University:University of California, BerkeleyCandidate:Ritter, Stephan JohannesFull Text:PDF
GTID:1458390005490863Subject:Biology
Abstract/Summary:
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.
Keywords/Search Tags:Package
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