QICD (version 1.2.0)

QICD.cv:

Description

Dose k-fold cross-validation for QICD, produces a plot and returns an appropriate tuning parameter lambda

Usage

QICD.cv(y, x, beta = NULL, tau, lambda, nfolds = 10,
a = 3.7, funname = "scad", intercept = TRUE, thresh = 1e-06, 
maxin = 100, maxout = 20, mc.cores=getOption("mc.cores", 1L), 
plot.off = F, ...)

Arguments

y

response y as in QICD.

x

x matrix as in QICD.

beta

beta vector as in QICD

tau

tau value as in QICD

lambda

a user supplied lambda sequence. A numerical vector, which will be used as a pool for tuning parameter searching

nfolds

number of folds - default is 10.

a

a value as in QICD

funname

funname character vector as in QICD

intercept

intercept logical value as in QICD

thresh

thresh threshold as in QICD

maxin

maxin as in QICD

maxout

maxout as in QICD

mc.cores

The number of cores to use for parallel computing, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set. Must be at least one, and parallelization requires at least two cores.

plot.off

a logical value to control if a plot of prediction error vs. lambda will be produced. Default is FALSE and a plot will be given.

…

other argument that can be passed to plot

Value

an object of class "cv.qicd" is returned, which is a list with the components of the cross-validation fit.

lambda

the values of lambda used in the fits.

cvm

The mean cross-validated error-a vetor of length nlambda as in QICD

cvsd

estimate of standard error of cvm.

cvup

upper curve = cvm+cvsd.

cvlo

upper curve = cvm-cvsd.

nzero

number of non-zero coefficients at each lambda

lambda.min

value of lambda that gives minimum cvm.

lambda.1se

largest value of lambda such that error is within 1 standard error of the minimum.

Details

The function run QICD nfolds times. For each specific lambda, the average test prediction error will be produced for comparison. Claim that QICD.cv does NOT search for values of a.

References

Peng,B and Wang,L. (2015)An Iterative Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression. Journal of Computational and Graphical Statistics http://amstat.tandfonline.com/doi/abs/10.1080/10618600.2014.913516 http://doi.org/10.1080/10618600.2014.913516

See Also

QICD,QICD.BIC

Examples

x=matrix(rnorm(1000),50)
n=dim(x)[1]
p=dim(x)[2]
intercept=1
y=x[,1]+x[,7]+x[,9]+0.1*rnorm(n)
beta1=rep(0,p+intercept)
tau=0.5
a=2.7
res.cv=QICD.cv(y, x, beta1, tau, lambda=seq(8,9,by=0.1),
nfolds=3, a, funname="scad",intercept=intercept)