A penalized robust semiparametric approach for gene-environment interactions. A selective review of robust variable selection with applications in bioinformatics. Robust network based regularization and variable selection for high dimensional genomics data in cancer prognosis. Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes. This package provides implementation for methods proposed in This update significantly increases the speed of cross-validation functions in this package.
Two new, easy to use, integrated interfaces: cv.regnet() and regnet().
Out = cv.regnet(X, Y, response="binary", penalty="network", folds=5, r = 4.5)ī = regnet(X, Y, "binary", "network", out$lambda, out$lambda, r = 4.5) List(tp=tp, fp=fp) Binary response Example.2 (Network Logistic) data(LogisticExample) Out = cv.regnet(X, Y, response="survival", penalty="network", clv=clv, robust=TRUE, verbo = TRUE)ī = regnet(X, Y, "survival", "network", out$lambda, out$lambda, clv=clv, robust=TRUE) Released versions of regnet are available on R CRAN (link), and can be installed within R via install.packages("regnet")Įxamples Survival response Example.1 (Robust Network) data(SurvExample)Ĭlv = c(1:5) # variable 1 to 5 are clinical variables, we choose not to penalize them here. To install from github, run these two lines of code in R install.packages("devtools") Functions for other regularization methods will be included in the forthcoming upgraded versions. Two recent additions are the robust network regularization for the survival response and the network regularization for continuous response. This package provides procedures of network-based variable selection for generalized linear models. Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features.
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