Package: ProSGPV 1.0.1
ProSGPV: Penalized Regression with Second-Generation P-Values
Implementation of penalized regression with second-generation p-values for variable selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.
Authors:
ProSGPV_1.0.1.tar.gz
ProSGPV_1.0.1.zip(r-4.7-any)ProSGPV_1.0.1.zip(r-4.6-any)ProSGPV_1.0.1.zip(r-4.5-any)
ProSGPV_1.0.1.tgz(r-4.6-any)ProSGPV_1.0.1.tgz(r-4.5-any)
ProSGPV_1.0.1.tar.gz(r-4.7-any)ProSGPV_1.0.1.tar.gz(r-4.6-any)
ProSGPV_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ProSGPV/json (API)
| # Install 'ProSGPV' in R: |
| install.packages('ProSGPV', repos = c('https://zuoyi93.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zuoyi93/prosgpv/issues
Last updated from:d07b8f77d2. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 126 | ||
| source / vignettes | OK | 197 | ||
| linux-release | OK | 164 | ||
| macos-release | OK | 232 | ||
| macos-oldrel | OK | 216 | ||
| windows-devel | OK | 96 | ||
| windows-release | OK | 88 | ||
| windows-oldrel | OK | 111 | ||
| wasm-release | OK | 91 |
Exports:gen.sim.datapro.sgpv
Dependencies:brglm2codetoolsenrichwithforeachglmnetiteratorslatticeMASSMatrixnleqslvnnetnumDerivRcppRcppEigenshapestatmodsurvival
Last update: 2021-08-24
Started: 2021-04-22
Last update: 2021-08-24
Started: 2021-03-28
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| 'coef.sgpv': Extract coefficients from the model fit | coef.sgpv |
| 'gen.sim.data': Generate simulation data | gen.sim.data |
| 'get.candidate': Get candidate set | get.candidate |
| 'get.coef': Get coefficients at each 'lambda' | get.coef |
| 'get.var': Get indices | get.var |
| 'gvif': Get GVIF for each variable | gvif |
| 'plot.sgpv': Plot variable selection results | plot.sgpv |
| 'predict.sgpv': Prediction using the fitted model | predict.sgpv |
| 'print.sgpv': Print variable selection results | print.sgpv |
| 'pro.sgpv' function | pro.sgpv |
| Spine data | spine |
| 'summary.sgpv': Summary of the final model | summary.sgpv |
| Tehran housing data | t.housing |
