| loclin {OSCV} | R Documentation |
Computing the local linear estimate (LLE).
Description
Computing the LLE based on data (desx,y) over the given vector of the argument values u. The Gausssian kernel is used. See expression (3) in Savchuk and Hart (2017).
Usage
loclin(u, desx, y, h)
Arguments
u |
numerical vector of argument values, |
desx |
numerical vecror of design points, |
y |
numerical vecror of data values (corresponding to the specified design points |
h |
numerical bandwidth value (scalar). |
Details
Computing the LLE based on the Gaussian kernel for the specified vector of the argument values u and given vectors of design points desx and the corresponding data values y.
Value
Numerical vector of the LLE values computed over the specified vector of u points.
References
Clevelend, W.S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829-836.
Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, doi:10.1007/s00180-017-0713-7.
See Also
OSCV_reg, h_OSCV_reg, ASE_reg, h_ASE_reg, CV_reg.
Examples
## Not run:
# Example (simulated data).
n=200
dx=(1:n-0.5)/n
regf=2*dx^10*(1-dx)^2+dx^2*(1-dx)^10
u=seq(0,1,len=1000)
ydat=regf+rnorm(n,sd=0.002)
dev.new()
plot(dx,regf,'l',lty="dashed",lwd=3,xlim=c(0,1),ylim=c(1.1*min(ydat),1.1*max(ydat)),
cex.axis=1.7,cex.lab=1.7)
title(main="Function, generated data, and LLE",cex.main=1.5)
points(dx,ydat,pch=20,cex=1.5)
lines(u,loclin(u,dx,ydat,0.05),lwd=3,col="blue")
legend(0,1.1*max(ydat),legend=c("LLE based on h=0.05","true regression function"),
lwd=c(2,3),lty=c("solid","dashed"),col=c("blue","black"),cex=1.5,bty="n")
legend(0.7,0.5*min(ydat),legend="n=200",cex=1.7,bty="n")
## End(Not run)