locpol (version 0.8.0)

simpleSmoothers: Simple smoother

Description

Computes simple kernel smoothing

Usage

simpleSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
  simpleSqSmootherC(x, y, xeval, bw, kernel)

Value

Both functions returns a data.frame with

x

\(x\) evaluation points.

reg

the smoothed values at x points.

...

Arguments

x

x covariate data values.

y

y response data values.

xeval

Vector with evaluation points.

bw

Smoothing parameter, bandwidth.

kernel

Kernel used to perform the estimation, see Kernels

weig

weights if they are required.

Author

Jorge Luis Ojeda Cabrera.

Details

Computes simple smoothing, that is to say: it averages y values times kernel evaluated on x values. simpleSqSmootherC does the average with the square of such values.

See Also

PRDenEstC, Kernel characteristics

Examples

	size <- 1000
	x <- runif(100)
	bw <- 0.125
	kernel <- EpaK
	xeval <- 1:9/10
	y <- rep(1,100)	
	##	x kern. aver. should give density f(x)
	prDen <- PRDenEstC(x,xeval,bw,kernel)$den
	ssDen <- simpleSmootherC(x,y,xeval,bw,kernel)$reg
	all(abs(prDen-ssDen)<1e-15)
	##	x kern. aver. should be f(x)*R2(K) aprox.
	s2Den <- simpleSqSmootherC(x,y,xeval,bw,kernel)$reg
	summary( abs(prDen*RK(kernel)-s2Den) )
	summary( abs(1*RK(kernel)-s2Den) )
	##	x kern. aver. should be f(x)*R2(K) aprox.
	for(n in c(1000,1e4,1e5))
	{
		s2D <- simpleSqSmootherC(runif(n),rep(1,n),xeval,bw,kernel)$reg
		cat("\n",n,"\n")
		print( summary( abs(1*RK(kernel)-s2D) ) )
	}