Description Usage Details Author(s) References

Internal PFMC functions

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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)
``` |

Functions from R package `ncvreg`

, which are adapted to allow the
argument `weights`

on samples.

Shijie Quan, Shun He

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

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