PFMC-internal: Internal PFMC functions

In scrcss/pfmc_pkg: IRLS-EM algorithm for the penalized finite-mixture Cox PH model

Description

Internal PFMC functions

Usage

cv.ncvsurv(X, y, ..., cluster, nfolds=10, seed, returnY=FALSE, trace=FALSE)
ncvsurv(X, y, penalty=c("MCP", "SCAD", "lasso"), gamma=switch(penalty, SCAD=3.7, 3),
        alpha=1, lambda.min=ifelse(n>p,.001,.05), nlambda=100, lambda, 
		eps=1e-4, max.iter=10000, convex=TRUE, dfmax=p, 
		penalty.factor=rep(1, ncol(X)),weights=rep(1,length(y[,1])), 
		warn=TRUE, returnX=FALSE, ...)
convexMin(b, X, penalty, gamma, l2, penalty.factor, a, Delta=NULL, weights)
ncvgetmin(lambda,cvm,cvsd)
lamNames(l)
setupLambdaCox(X, y, Delta, alpha, lambda.min, nlambda, 
           penalty.factor, weights=rep(1,length(Delta))) 
std(X, weights)

Details

Functions from R package ncvreg, which are adapted to allow the argument weights on samples.

Author(s)

Shijie Quan, Shun He

References

R package ncvreg, https://cran.r-project.org/web/packages/ncvreg/

Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.

Subtype classification and heterogeneous prognosis model construction in precision medicine. Na You, Shun He, Xueqin Wang, Junxian Zhu and Heping Zhang

scrcss/pfmc_pkg documentation built on May 8, 2019, 2:33 a.m.