solid.mcv.fun: Modified cross validation
In ChuanHong/solid-copy: A Divide-and-Conquer Method for Sparse Risk Prediction and Evaluation

Description Usage Arguments Details Value Modified Cross Validation Author(s) References Examples

A screening and one-step linearization infused DAC

1	solid.mcv.fun(dat.list, niter, ridge)

`dat.list`	dataset
`niter`	number of iterations
`ridge`	using ridge penalty for initial value

Modified cross validation for SOLID method

auc.mcv returns auc obtained by modified cross validation auc.mcv.se returns the standard error for auc obtained by modified cross validation roc.mcv.mtx returns the accuracy table obtained by modified cross validation

To assess the accuracy of a predictive re- gression model, we develop a modified cross-validation (MCV) that utilizes the side products of the SOLID hence substantially reduce the computational burden. Compared with existing DAC methods, the MCV procedure is the first effort to make inference on accuracy.

Chuan Hong

Hong, C., Wang, Y. and Cat T. (2019). A Divide-and-Conquer Method for Sparse Risk Prediction and Evaluation (under revision).

N=1e5
p.x=50
K=10
n=N/K
niter=3
cor=0
b0=-8
bb = c(1, 0.8, 0.4, 0.2, 0.1)
beta0 = c(b0, bb, rep(0, p.x - length(bb)))
dat.list=sim.list.fun(nn=N,K=10,p.x=50,cor=0.2,beta0)
SOLID.mcv=solid.mcv.fun(dat.list, niter=3)