Movatterモバイル変換

This package provides efficient implementations of cross-validationtechniques for linear and ridge regression models, leveraging C++ codewith Rcpp, RcppParallel, and Eigen libraries. It supports leave-one-out,generalized, and K-fold cross-validation methods, utilizing Eigenmatrices for high performance.

Dependencies

• Rcpp: Integrationbetween R and C++.
• RcppParallel:Parallel computing support for Rcpp.
• RcppEigen:Integration between R and Eigen C++ library.

Requirements

• R
• Rcpp
• RcppParallel
• RcppEigen

Acknowledgments

This code is adapted and extended from various sources, leveragingthe capabilities of the following:

• Rcpp by DirkEddelbuettel, Romain Francois, et al., for R and C++ integration.
• RcppParallelby Romain Francois, et al., for parallel computing support in Rcpp.
• RcppEigen byDouglas Bates, Romain Francois, et al., for integration between R andEigen C++ library.

Please refer to the source files for detailed information andlicenses.

Contributors

• [Philip Nye]:GitHubProfile

License

This code is underMIT License.

Example Usage

library(cvLM)data(mtcars)n<-nrow(mtcars)# Formula methodcvLM(  mpg~ .,data = mtcars,K.vals = n,# Leave-one-out CVlambda =10# Shrinkage parameter of 10)# lm methodmy.lm<-lm(mpg~ .,data = mtcars)cvLM(  my.lm,data = mtcars,K.vals =c(5L, 8L),# Perform both 5- and 8-fold CVn.threads = 8L,# Allow up to 8 threads for computationseed = 1234L)# glm methodmy.glm<-glm(mpg~ .,data = mtcars)cvLM(  my.glm,data = mtcars,K.vals = n,generalized =TRUE# Use generalized CV)