npreg: Nonparametric regression based on data with unknown...

In decon: Deconvolution Estimation in Measurement Error Models

Description

To compute nonparametric regression line from data contaminated with measurement error. The measurement error type is unknown.

Usage

 npreg(w, y, e, bw, adjust = 1, n = 512, from, to, cut = 0, na.rm = FALSE,...) 

Arguments

w,y	The observed data W and Y. It is a vector of length at least 3.
e	Observed vector of measurement errors.
bw	Smoothing parameter.
adjust	adjust the range there the PDF is to be evaluated. By default, adjust=1.
n	number of points where the PDF is to be evaluated.
from	the starting point where the PDF is to be evaluated.
to	the starting point where the PDF is to be evaluated.
cut	used to adjust the starting end ending points where the PDF is to be evaluated.
na.rm	is set to FALSE by default: no NA value is allowed.
...	controls

Details

The optimal bandwidth is selected by minimizing abs(Var(f.hat)+Var(E)-Var(Y)).

Value

An object of class “Decon”.

References

Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.

See Also

DeconCdf, DeconNpr, DeconCPdf.

Examples

n <- 2000
x <- c(rnorm(n/2,2,1), rnorm(n/2, -2,1))
sig <- .8
u <- sig*rnorm(n)
w <- x+u
e <- rnorm(n, sd=0.2)
y <- x^2-2*x+e
bw1 <- bw.dboot1(w, sig)
u0 <- sig*rnorm(n/2) # typically the size of u0 is smaller than x.
m2 <- npreg(w, y, u0, from=0.9*min(x), to=0.9*max(x))

# plot the results
plot(m2, col="red", lwd=3, lty=2, xlab="x", ylab="m(x)", main="", 
			zero.line=FALSE)
lines(ksmooth(x,y, kernel = "normal", 2, range.x=c(0.9*min(x),0.9*max(x))), 
			lwd=3, lty=1)
lines(ksmooth(w,y, kernel = "normal", 2, range.x=c(0.9*min(x),0.9*max(x))), 
			col="blue", lwd=3, lty=3)

decon documentation built on Oct. 21, 2021, 1:07 a.m.