PUlasso-package | R Documentation |
The package efficiently solves PU problem in low or high dimensional setting using Maximization-Minorization and (block) coordinate descent. It allows simultaneous feature selection and parameter estimation for classification. Sparse calculation and parallel computing are supported for the further computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <https://arxiv.org/abs/1711.08129>.
Main functions: grpPUlasso, cv.grpPUlasso, coef, predict
Hyebin Song, hsong@stat.wisc.edu, Garvesh Raskutti, raskutti@stat.wisc.edu.
Useful links:
data("simulPU")
fit<-grpPUlasso(X=simulPU$X,z=simulPU$z,py1=simulPU$truePY1)
## Not run:
cvfit<-cv.grpPUlasso(X=simulPU$X,z=simulPU$z,py1=simulPU$truePY1)
## End(Not run)
coef(fit,lambda=fit$lambda[10])
predict(fit,newdata = head(simulPU$X), lambda= fit$lambda[10],type = "response")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.