QICD.cv: Cross-validation for QICD
In geogria/QICD: Estimate the Coefficients for Non-Convex Penalized Quantile Regression Model by using QICD Algorithm

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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, ...)

`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`

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.

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.

Bo Peng

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 doi: 10.1080/10618600.2014.913516

QICD,QICD.BIC

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)