relsharp_mean_c {sharpPen}

Ridge/Enet/LASSO Sharpening via the Mean and then applying the residual sharpening method.

Description

This is a function to shrink responses towards their mean and then apply residual sharpening as a form of data sharpening to remove roughness, prior to use in local polynomial regression.

Usage

 relsharp_mean_c(x, y, alpha, hband) 

Arguments

x

numeric vector of equally spaced x data. Missing values are not accepted.

y

vector of y data. Missing values are not accepted.

alpha

the elasticnet mixing parameter vector, with alpha in [0,1].

hband

the kernel bandwidth smoothing parameter, which will be used in the residual sharpening method.

Details

Note that the predictor values are assumed to be equally spaced.

Value

numeric matrix of sharpened responses, with each column corresponding to different values of alpha

Author(s)

D.Wang

Examples

x<-seq(0,10,length=100)
g <- function(x) sin(x)
y<-g(x)+rnorm(100)
ys<-relsharp_mean_c(x, y,alpha=c(0.2,0.8), dpill(x,y))
y.lp2<-locpoly(x,ys[,1],bandwidth=dpill(x,y),degree=1,gridsize=100)
y.lp8<-locpoly(x,ys[,2],bandwidth=dpill(x,y),degree=1,gridsize=100)
y.lp<-locpoly(x,y,bandwidth=dpill(x,y),degree=1,gridsize=100)
curve(g,x,xlim=c(0,10))
lines(y.lp2,col=2)
lines(y.lp8,col=3)
lines(y.lp,col=5)
norm(as.matrix(g(x) - y.lp2$y),type="2")
norm(as.matrix(g(x) - y.lp8$y),type="2")
norm(as.matrix(g(x) - y.lp$y),type="2")