Modified cross validation

A screening and one-step linearization infused DAC

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

Arguments

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

Details

Modified cross validation for SOLID method

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.

Value

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

References

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

Author

Chuan Hong

Examples

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)
#> Warning: collapsing to unique 'x' values
#> Warning: collapsing to unique 'x' values
#> Warning: collapsing to unique 'x' values