loclin: Computing the local linear estimate (LLE).
In OSCV: One-Sided Cross-Validation

Description Usage Arguments Details Value References See Also Examples

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).

1	loclin(u, desx, y, h)

`u`	numerical vector of argument values,
`desx`	numerical vecror of design points,
`y`	numerical vecror of data values (corresponding to the specified design points desx),
`h`	numerical bandwidth value (scalar).

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.

Numerical vector of the LLE values computed over the specified vector of u points.

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.

OSCV_reg, h_OSCV_reg, ASE_reg, h_ASE_reg, CV_reg.

## 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)