Package 'locpol' reference manual

Title:	Kernel Local Polynomial Regression
Description:	Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators.
Authors:	Jorge Luis Ojeda Cabrera [aut, cre], Bastiaan Quast [ctb]
Maintainer:	Jorge Luis Ojeda Cabrera <[email protected]>
License:	GPL (>= 2)
Version:	0.8.0
Built:	2024-11-06 06:38:06 UTC
Source:	CRAN

Bivariate Local estimation.

Description

Simple bivariate Local density and regression estimation with weights.

Usage

    bivDens(X,weig,K,H)
    bivReg(X,Y,weig,K,H)
    ## S3 method for class 'bivNpEst'
predict(object,newdata,...)
    ## S3 method for class 'bivNpEst'
plot(x,...)
bivDens(X,weig,K,H)
    bivReg(X,Y,weig,K,H)
    ## S3 method for class 'bivNpEst'
predict(object,newdata,...)
    ## S3 method for class 'bivNpEst'
plot(x,...)

Arguments

`X`	Covariate or independent data, should be a `data.frame` or `matrix`, whose two first two columns are used.
`Y`	Response data, a `vector`

`weig`	Vector of weigths for each observations.
`K`	Bivariate kernel function as `bivDens` and `bivReg`.
`H`	Bandwidth matrix. Its default value is determined by `mayBeBwSel`.
`object`, `x`	`bivNpEst` class objects, those returned by `bivDens` and `bivReg` functions.
`newdata`	Data, should be a `data.frame` where the density or regressions is going to be predicted.
`...`	Further graphical parameters. These parameters should agree with those in `persp`.

Details

The functions bivDens and bivReg provide a very basic interface that allows bivariate local estimation with weights. It implements basic kernel density estimator and Nadaraya–Watson estimator for bivariate data. Very simple interface methods allow the prediction and plotting of these estimators.

The only bivariate kernels provided are epaK2d and gauK2d. New ones can be added in the same way as functions with a vector of length 2.

The default bandwidth selector (see mayBeBwSel) that has been provided is not optimal or good in any sense. It has been added as a simple way to provide an easy, fast and simple way to be able to use the estimators.

The graphical parameters allowed for ... in plot(x,...) are those that appears in the function persp. The list plotBivNpEstOpts provide a default for some of these graphical parameters.

Value

A list containing:

`X`	Covariate data.
`Y`	Response data
`H`	Bandwidth matrix
`estFun`	Estimator function.

Author(s)

Jorge Luis Ojeda Cabrera.

Examples

  n <- 100
  d <- data.frame(x=rexp(n,rate=1/2),y=rnorm(n))
  ## x is a length-biased version of an exp. dist. with rate 1.
  dDen <- bivDens(d,weig=1/d$x)
  plot(dDen,r=5)
  d <- data.frame(X1=runif(n),X2=runif(n))
  d$Y <-  exp(10*d$X1+d$X2^2) 
  dDen <- bivDens(d[,c("X1","X2")])
  plot(dDen,r=5)
  dReg <- bivReg(d[,c("X1","X2")],d$Y)
  plot(dReg,r=5)
  plot(dReg,r=5,phi=20,theta=40)
n <- 100
  d <- data.frame(x=rexp(n,rate=1/2),y=rnorm(n))
  ## x is a length-biased version of an exp. dist. with rate 1.
  dDen <- bivDens(d,weig=1/d$x)
  plot(dDen,r=5)
  d <- data.frame(X1=runif(n),X2=runif(n))
  d$Y <-  exp(10*d$X1+d$X2^2) 
  dDen <- bivDens(d[,c("X1","X2")])
  plot(dDen,r=5)
  dReg <- bivReg(d[,c("X1","X2")],d$Y)
  plot(dReg,r=5)
  plot(dReg,r=5,phi=20,theta=40)

Compute kernel values.

Description

Some R code provided to compute kernel related values.

Usage

	computeRK(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	computeK4(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	computeMu(i, kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	computeMu0(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	Kconvol(kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
			subdivisions = 25)
computeRK(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	computeK4(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	computeMu(i, kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	computeMu0(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
			subdivisions = 25)
	Kconvol(kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
			subdivisions = 25)

Arguments

`kernel`	Kernel used to perform the estimation, see `Kernels`
`i`	Order of kernel moment to compute
`lower`, `upper`	Integration limits.
`subdivisions`	the maximum number of subintervals.

Details

These functions uses function integrate.

Value

A numeric value returning:

`computeK4`	The fourth order autoconvolution of `K`.
`computeRK`	The second order autoconvolution of `K`.
`computeMu0`	The integral of `K`.
`computeMu2`	The second order moment of `K`.
`computeMu`	The $i$ -th order moment of `K`.
`Kconvol`	The autoconvolution of `K`.

These functions are implemented by means of integrate.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	##	Note that lower and upper params are set in the definition to
	##	use 'dom()' function.
	g <- function(kernels)
	{
		mu0 <- sapply(kernels,function(x) computeMu0(x,))
		mu0.ok <- sapply(kernels,mu0K)
		mu2 <- sapply(kernels,function(x) computeMu(2,x))
		mu2.ok <- sapply(kernels,mu2K)
		Rk.ok <- sapply(kernels,RK)
		RK <- sapply(kernels,function(x) computeRK(x))
		K4 <- sapply(kernels,function(x) computeK4(x))
		res <- data.frame(mu0,mu0.ok,mu2,mu2.ok,RK,Rk.ok,K4)
		res
	}
	g(kernels=c(EpaK,gaussK,TriweigK,TrianK))
##	Note that lower and upper params are set in the definition to
	##	use 'dom()' function.
	g <- function(kernels)
	{
		mu0 <- sapply(kernels,function(x) computeMu0(x,))
		mu0.ok <- sapply(kernels,mu0K)
		mu2 <- sapply(kernels,function(x) computeMu(2,x))
		mu2.ok <- sapply(kernels,mu2K)
		Rk.ok <- sapply(kernels,RK)
		RK <- sapply(kernels,function(x) computeRK(x))
		K4 <- sapply(kernels,function(x) computeK4(x))
		res <- data.frame(mu0,mu0.ok,mu2,mu2.ok,RK,Rk.ok,K4)
		res
	}
	g(kernels=c(EpaK,gaussK,TriweigK,TrianK))

CV bandwidth selector for density

Description

Computes Cross Validation bandwidth selector for the Parzen–Rosenblatt density estimator...

Usage

denCVBwSelC(x, kernel = gaussK, weig = rep(1, length(x)),
            interval = .lokestOptInt)
denCVBwSelC(x, kernel = gaussK, weig = rep(1, length(x)),
            interval = .lokestOptInt)

Arguments

`x`	vector with data points.
`kernel`	Kernel used to perform the estimation, see `Kernels`.
`weig`	Vector of weights for observations.
`interval`	A range of values where to look for the bandwidth parameter.

Details

The selector is implemented using its definition.

Value

A numeric value with the bandwidth.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

stdy <- function(size=100,rVar=rnorm,dVar=dnorm,kernel=gaussK,x=NULL)
{
	if( is.null(x) ) x <- rVar(size)
	Tc <- system.time( dbwc <- denCVBwSelC(x,kernel) )[3]
	ucvT <- system.time( ucvBw <- bw.ucv(x,lower=0.00001,upper=2.0) )[3]
	nrdT <- system.time( nrdBw <- bw.nrd(x) )[3]
	{
		xeval <- seq( min(x)+dbwc , max(x)-dbwc ,length=50)
		hist(x,probability=TRUE)
		lines(xeval,trueDen <- dVar(xeval),col="red")
		lines(density(x),col="cyan")
		xevalDenc <- PRDenEstC(x,xeval,dbwc,kernel)
		dencMSE <- mean( (trueDen-xevalDenc)^2 )
		xevalucvDen <- PRDenEstC(x,xeval,ucvBw,kernel)
		ucvMSE <- mean( (trueDen-xevalucvDen)^2 )
		xevalDenNrd <- PRDenEstC(x,xeval,nrdBw,kernel)
		nrdMSE <- mean( (trueDen-xevalDenNrd)^2 )
		lines(xevalDenc,col="green")
		lines(xevalucvDen,col="blue")
		lines(xevalDenNrd,col="grey")
	}
	return( cbind(  bwVal=c(evalC=dbwc,ucvBw=ucvBw,nrdBw=nrdBw),
				mse=c(dencMSE,ucvMSE,nrdMSE),
				time=c(Tc,ucvT,nrdT) ) )
}
stdy(100,kernel=gaussK)
stdy(100,rVar=rexp,dVar=dexp,kernel=gaussK)
##	check stdy with other kernel, distributions
stdy <- function(size=100,rVar=rnorm,dVar=dnorm,kernel=gaussK,x=NULL)
{
	if( is.null(x) ) x <- rVar(size)
	Tc <- system.time( dbwc <- denCVBwSelC(x,kernel) )[3]
	ucvT <- system.time( ucvBw <- bw.ucv(x,lower=0.00001,upper=2.0) )[3]
	nrdT <- system.time( nrdBw <- bw.nrd(x) )[3]
	{
		xeval <- seq( min(x)+dbwc , max(x)-dbwc ,length=50)
		hist(x,probability=TRUE)
		lines(xeval,trueDen <- dVar(xeval),col="red")
		lines(density(x),col="cyan")
		xevalDenc <- PRDenEstC(x,xeval,dbwc,kernel)
		dencMSE <- mean( (trueDen-xevalDenc)^2 )
		xevalucvDen <- PRDenEstC(x,xeval,ucvBw,kernel)
		ucvMSE <- mean( (trueDen-xevalucvDen)^2 )
		xevalDenNrd <- PRDenEstC(x,xeval,nrdBw,kernel)
		nrdMSE <- mean( (trueDen-xevalDenNrd)^2 )
		lines(xevalDenc,col="green")
		lines(xevalucvDen,col="blue")
		lines(xevalDenNrd,col="grey")
	}
	return( cbind(  bwVal=c(evalC=dbwc,ucvBw=ucvBw,nrdBw=nrdBw),
				mse=c(dencMSE,ucvMSE,nrdMSE),
				time=c(Tc,ucvT,nrdT) ) )
}
stdy(100,kernel=gaussK)
stdy(100,rVar=rexp,dVar=dexp,kernel=gaussK)
##	check stdy with other kernel, distributions

Equivalent Kernel.

Description

Computes the Equivalent kernel for the local polynomial estimation.

Usage

equivKernel(kernel,nu,deg,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
	subdivisions=25)
equivKernel(kernel,nu,deg,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
	subdivisions=25)

Arguments

`nu`	Orders of derivative to estimate.
`deg`	Degree of Local polynomial estimator.
`kernel`	Kernel used to perform the estimation, see `Kernels`
`lower`, `upper`	Integration limits.
`subdivisions`	the maximum number of subintervals.

Details

The definition of the Equivalent kernel for the local polynomial estimation can be found in page 64 in Fan and Gijbels(1996). The implementation uses computeMu to compute matrix $S$ and then returns a function object

Value

Returns a vector whose components are the equivalent kernel used to compute the local polynomial estimator for the derivatives in nu.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Examples

##	Some kernels and equiv. for higher order
##	compare with p=1
curve(EpaK(x),-3,3,ylim=c(-.5,1))
f <- equivKernel(EpaK,0,3)
curve(f(x),-3,3,add=TRUE,col="blue")
curve(gaussK(x),-3,3,add=TRUE)
f <- equivKernel(gaussK,0,3)
curve(f(x),-3,3,add=TRUE,col="blue")
##	Draw several Equivalent locl polynomial kernels
curve(EpaK(x),-3,3,ylim=c(-.5,1))
for(p in 1:5){
	curve(equivKernel(gaussK,0,p)(x),-3,3,add=TRUE)
    }
##	Some kernels and equiv. for higher order
##	compare with p=1
curve(EpaK(x),-3,3,ylim=c(-.5,1))
f <- equivKernel(EpaK,0,3)
curve(f(x),-3,3,add=TRUE,col="blue")
curve(gaussK(x),-3,3,add=TRUE)
f <- equivKernel(gaussK,0,3)
curve(f(x),-3,3,add=TRUE,col="blue")
##	Draw several Equivalent locl polynomial kernels
curve(EpaK(x),-3,3,ylim=c(-.5,1))
for(p in 1:5){
	curve(equivKernel(gaussK,0,p)(x),-3,3,add=TRUE)
    }

Kernel characteristics

Description

For a given kernel these functions return some of the most commonly used numeric values related to them.

Usage

	RK(K)
	RdK(K)
	mu2K(K)
	mu0K(K)
	K4(K)
	dom(K)
RK(K)
	RdK(K)
	mu2K(K)
	mu0K(K)
	K4(K)
	dom(K)

Arguments

`K`	A kernel as given in `Kernels`

Details

Most of these functions are implemented as an attribute of every kernel. For the computations of the numeric value for these quantities, see references.

Value

A numeric value returning:

`RK`	The $L_2$ norm of `K`.
`RdK`	The $L_2$ norm of the derivative of `K`.
`mu2K`	The second order moment of `K`.
`mu2K`	The zeroth order moment of `K`.
`dom`	The support of `K`.
`K4`	The fourth order autoconvolution of `K` at $x=0$ .

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	##	Note that lower and upper params are set in the definition to
	##	use 'dom()' function.
	g <- function(kernels)
	{
		mu0 <- sapply(kernels,function(x) computeMu0(x,))
		mu0.ok <- sapply(kernels,mu0K)
		mu2 <- sapply(kernels,function(x) computeMu(2,x))
		mu2.ok <- sapply(kernels,mu2K)
		Rk.ok <- sapply(kernels,RK)
		RK <- sapply(kernels,function(x) computeRK(x))
		K4 <- sapply(kernels,function(x) computeK4(x))
		res <- data.frame(mu0,mu0.ok,mu2,mu2.ok,RK,Rk.ok,K4)
		res
	}
	g(kernels=c(EpaK,gaussK,TriweigK,TrianK))
##	Note that lower and upper params are set in the definition to
	##	use 'dom()' function.
	g <- function(kernels)
	{
		mu0 <- sapply(kernels,function(x) computeMu0(x,))
		mu0.ok <- sapply(kernels,mu0K)
		mu2 <- sapply(kernels,function(x) computeMu(2,x))
		mu2.ok <- sapply(kernels,mu2K)
		Rk.ok <- sapply(kernels,RK)
		RK <- sapply(kernels,function(x) computeRK(x))
		K4 <- sapply(kernels,function(x) computeK4(x))
		res <- data.frame(mu0,mu0.ok,mu2,mu2.ok,RK,Rk.ok,K4)
		res
	}
	g(kernels=c(EpaK,gaussK,TriweigK,TrianK))

Kernel Constants used in Bandwidth Selection.

Description

These are values depending on the kernel and the local polynomial degrees that are used in bandwidth selection, as proposed in Fan and Gijbels(1996).

Usage

cteNuK(nu,p,kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
	subdivisions= 25)
adjNuK(nu,p,kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
	subdivisions= 25)
cteNuK(nu,p,kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
	subdivisions= 25)
adjNuK(nu,p,kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
	subdivisions= 25)

Arguments

`nu`	Order of derivative to estimate.
`p`	Degree of Local polynomial estimator.
`kernel`	Kernel used to perform the estimation, see `Kernels`
`lower`, `upper`	Integration limits.
`subdivisions`	the maximum number of subintervals.

Details

cteNuK is computed using Compute kernel values and link{equivKernel} jointly with the numerical integration utility integrate. adjNuK is implemented using quotients of previous functions. See Fan and Gijbels(1996) pages 67 and 119.

Value

Both functions returns numeric values.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Kernels.

Description

Definition of common kernels used in local polynomial estimation.

Usage

	CosK(x)
	EpaK(x)
	Epa2K(x)
	gaussK(x)
CosK(x)
	EpaK(x)
	Epa2K(x)
	gaussK(x)

Arguments

`x`	Numeric vector o value.

Details

The implementation of these kernels is done by means functions that can operate on vectors.

Most common referred numeric values for these kernels are provided as attributes, see RK, mu0K, etc....

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Local Polynomial Weights

Description

Local Constant and local Linear estimator with weight.

Usage

  locCteWeightsC(x, xeval, bw, kernel, weig = rep(1, length(x)))
  locLinWeightsC(x, xeval, bw, kernel, weig = rep(1, length(x)))
  locPolWeights(x, xeval, deg, bw, kernel, weig = rep(1, length(x)))
  locWeightsEval(lpweig, y)
  locWeightsEvalC(lpweig, y)
locCteWeightsC(x, xeval, bw, kernel, weig = rep(1, length(x)))
  locLinWeightsC(x, xeval, bw, kernel, weig = rep(1, length(x)))
  locPolWeights(x, xeval, deg, bw, kernel, weig = rep(1, length(x)))
  locWeightsEval(lpweig, y)
  locWeightsEvalC(lpweig, y)

Arguments

`x`	x covariate data values.
`y`	y response data values.
`xeval`	Vector with evaluation points.
`bw`	Smoothing parameter, bandwidth.
`deg`	Local polynomial estimation degree ( $p$ ).
`kernel`	Kernel used to perform the estimation, see `Kernels`
`weig`	Vector of weights for observations.
`lpweig`	Local polynomial weights $(X^TWX)^{-1}X^TW$ evaluated at `xeval` matrix.

Details

locCteWeightsC and locLinWeightsC computes local constant and local linear weights, say any of the entries of the vector $(X^TWX)^{-1}X^TW$ for $p=0$ and $p=1$ resp. locWeightsEvalC and locWeightsEval computes local the estimator for a given vector of responses y

Value

locCteWeightsC and locLinWeightsC returns a list with two components

`den`	Estimation of $(nhf(x))^{p+1}$ being $h$ the bandwidth `bw`.
`locWeig`	$(X^TWX)^{-1}X^TW$ evaluated at `xeval` Matrix.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	bw <- .25
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	lcw <- locCteWeightsC(d$x, xeval, bw, kernel)$locWeig
	lce <- locWeightsEval(lcw, y)
	lceB <- locCteSmootherC(d$x, d$y, xeval, bw, kernel)$beta0
	mean((lce-lceB)^2)
    llw <- locLinWeightsC(d$x, xeval, bw, kernel)$locWeig
	lle <- locWeightsEval(llw, y)
	lleB <- locLinSmootherC(d$x, d$y, xeval, bw, kernel)$beta0
	mean((lle-lleB)^2)
size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	bw <- .25
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	lcw <- locCteWeightsC(d$x, xeval, bw, kernel)$locWeig
	lce <- locWeightsEval(lcw, y)
	lceB <- locCteSmootherC(d$x, d$y, xeval, bw, kernel)$beta0
	mean((lce-lceB)^2)
    llw <- locLinWeightsC(d$x, xeval, bw, kernel)$locWeig
	lle <- locWeightsEval(llw, y)
	lleB <- locLinSmootherC(d$x, d$y, xeval, bw, kernel)$beta0
	mean((lle-lleB)^2)

Local Polynomial estimation.

Description

Formula interface for the local polynomial estimation.

Usage

    locpol(formula,data,weig=rep(1,nrow(data)),bw=NULL,kernel=EpaK,deg=1,
            xeval=NULL,xevalLen=100)
    confInterval(x)
    ## S3 method for class 'locpol'
residuals(object,...)
    ## S3 method for class 'locpol'
fitted(object,deg=0,...)
    ## S3 method for class 'locpol'
summary(object,...)
    ## S3 method for class 'locpol'
print(x,...)
    ## S3 method for class 'locpol'
plot(x,...)
locpol(formula,data,weig=rep(1,nrow(data)),bw=NULL,kernel=EpaK,deg=1,
            xeval=NULL,xevalLen=100)
    confInterval(x)
    ## S3 method for class 'locpol'
residuals(object,...)
    ## S3 method for class 'locpol'
fitted(object,deg=0,...)
    ## S3 method for class 'locpol'
summary(object,...)
    ## S3 method for class 'locpol'
print(x,...)
    ## S3 method for class 'locpol'
plot(x,...)

Arguments

`formula`	formula as in `lm`, only first covariate is used.
`data`	data frame with data.
`weig`	Vector of weights for each observations.
`bw`	Smoothing parameter, bandwidth.
`kernel`	Kernel used to perform the estimation, see `Kernels`
`deg`	Local polynomial estimation degree ( $p$ ).
`xeval`	Vector of evaluation points. By default `xevalLen` points between the min. and the max. of the regressors.
`xevalLen`	Length of `xeval` if it is `NULL`
`x`	A `locpol` object.
`object`	A `locpol` object.
`...`	Any other required argument.

Details

This is an interface to the local polynomial estimation function that provides basic lm functionality. summary and print methods shows very basic information about the fit, fitted return the estimation of the derivatives if deg is larger than 0, and plot provides a plot of data, local polynomial estimation and the variance estimation.

Variance estimation is carried out by means of the local constant regression estimation of the squared residuals.

confInterval provides confidence intervals for all points in x$lpFit$[,x$X], say those in xeval.

Value

A list containing among other components:

`mf`	Model frame for `data` and `formula`.
`data`	data frame with data.
`weig`	Vector of weight for each observations.
`xeval`	Vector of evaluation points.
`bw`	Smoothing parameter, bandwidth.
`kernel`	Kernel used, see `Kernels`
`KName`	Kernel name, a string with the name of kernel.
`deg`	Local polynomial estimation degree ( $p$ ).
`X`, `Y`	Names in `data` of the response and covariate. They are also used in `lpFit` to name the fitted data.
`residuals`	Residuals of the local polynomial fit.
`lpFit`	Data frame with the local polynomial fit. It contains covariate, response, derivatives estimation, $X$ density estimation, and variance estimation.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Crist'obal, J. A. and Alcal\'a, J. T. (2000). Nonparametric regression estimators for length biased data\/. J. Statist. Plann. Inference, 89, pp. 145-168.

Ahmad, Ibrahim A. (1995) On multivariate kernel estimation for samples from weighted distributions\/. Statistics & Probability Letters, 22, num. 2, pp. 121-129

Examples

    N <- 250
    xeval <- 0:100/100
    ##  ex1
    d <- data.frame(x = runif(N))
    d$y <- d$x^2 - d$x + 1 + rnorm(N, sd = 0.1)
    r <- locpol(y~x,d)
    plot(r)
    ##  ex2
    d <- data.frame(x = runif(N))
    d$y <- d$x^2 - d$x + 1 + (1+d$x)*rnorm(N, sd = 0.1)
    r <- locpol(y~x,d)
    plot(r)
    ## notice:
    rr <- locpol(y~x,d,xeval=runif(50,-1,1))
    ## notice x has null dens. outside (0,1)
    ## plot(rr) raises an error, no conf. bands outside (0,1).
    ## length biased data !!
    d <- data.frame(x = runif(10*N))
    d$y <- d$x^2 - d$x + 1 + (rexp(10*N,rate=4)-.25)
    posy <- d$y[ whichYPos <- which(d$y>0) ];
    d <- d[sample(whichYPos, N,prob=posy,replace=FALSE),]
    rBiased <- locpol(y~x,d)
    r <- locpol(y~x,d,weig=1/d$y)
    plot(d)
    points(r$lpFit[,r$X],r$lpFit[,r$Y],type="l",col="blue")
    points(rBiased$lpFit[,rBiased$X],rBiased$lpFit[,rBiased$Y],type="l")
    curve(x^2 - x + 1,add=TRUE,col="red")

N <- 250
    xeval <- 0:100/100
    ##  ex1
    d <- data.frame(x = runif(N))
    d$y <- d$x^2 - d$x + 1 + rnorm(N, sd = 0.1)
    r <- locpol(y~x,d)
    plot(r)
    ##  ex2
    d <- data.frame(x = runif(N))
    d$y <- d$x^2 - d$x + 1 + (1+d$x)*rnorm(N, sd = 0.1)
    r <- locpol(y~x,d)
    plot(r)
    ## notice:
    rr <- locpol(y~x,d,xeval=runif(50,-1,1))
    ## notice x has null dens. outside (0,1)
    ## plot(rr) raises an error, no conf. bands outside (0,1).
    ## length biased data !!
    d <- data.frame(x = runif(10*N))
    d$y <- d$x^2 - d$x + 1 + (rexp(10*N,rate=4)-.25)
    posy <- d$y[ whichYPos <- which(d$y>0) ];
    d <- d[sample(whichYPos, N,prob=posy,replace=FALSE),]
    rBiased <- locpol(y~x,d)
    r <- locpol(y~x,d,weig=1/d$y)
    plot(d)
    points(r$lpFit[,r$X],r$lpFit[,r$Y],type="l",col="blue")
    points(rBiased$lpFit[,rBiased$X],rBiased$lpFit[,rBiased$Y],type="l")
    curve(x^2 - x + 1,add=TRUE,col="red")

Local Polynomial estimation.

Description

Computes the local polynomial estimation of the regression function.

Usage

locCteSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
locLinSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
locCuadSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
locPolSmootherC(x, y, xeval, bw, deg, kernel, DET = FALSE,
	weig = rep(1, length(y)))
looLocPolSmootherC(x, y, bw, deg, kernel, weig = rep(1, length(y)),
        DET = FALSE)
locCteSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
locLinSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
locCuadSmootherC(x, y, xeval, bw, kernel, weig = rep(1, length(y)))
locPolSmootherC(x, y, xeval, bw, deg, kernel, DET = FALSE,
	weig = rep(1, length(y)))
looLocPolSmootherC(x, y, bw, deg, kernel, weig = rep(1, length(y)),
        DET = FALSE)

Arguments

`x`	x covariate data values.
`y`	y response data values.
`xeval`	Vector of evaluation points.
`bw`	Smoothing parameter, bandwidth.
`kernel`	Kernel used to perform the estimation, see `Kernels`
`weig`	Vector of weights for observations.
`deg`	Local polynomial estimation degree ( $p$ ).
`DET`	Boolean to ask for the computation of the determinant if the matrix $X^TWX$ .

Details

All these function perform the estimation of the regression function for different degrees. While locCteSmootherC, locLinSmootherC, and locCuadSmootherC uses direct computations for the degrees 0,1 and 2 respectively, locPolSmootherC implements a general method for any degree. Particularly useful can be looLocPolSmootherC(Leave one out) which computes the local polynomial estimator for any degree as locPolSmootherC does, but estimating $m(x_i)$ without using $i$ –th observation on the computation.

Value

A data frame whose components gives the evaluation points, the estimator for the regression function $m(x)$ and its derivatives at each point, and the estimation of the marginal density for x to the $p+1$ power. These components are given by:

`x`	Evaluation points.
`beta0`, `beta1`, `beta2`, `...`	Estimation of the $i$ -th derivative of the regression function ( $m^{(i)}(x)$ ) for $i=0,1,...$ .
`den`	Estimation of $(nhf(x))^{p+1}$ , being $h$ the bandwidth `bw`.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

N <- 100
xeval <- 0:10/10
d <- data.frame(x = runif(N))
bw <- 0.125
fx <- xeval^2 - xeval + 1
##	Non random
d$y <- d$x^2 - d$x + 1
cuest <- locCuadSmootherC(d$x, d$y ,xeval, bw, EpaK)
lpest2 <- locPolSmootherC(d$x, d$y , xeval, bw, 2, EpaK)
print(cbind(x = xeval, fx, cuad0 = cuest$beta0,
lp0 = lpest2$beta0, cuad1 = cuest$beta1, lp1 = lpest2$beta1))
##	Random
d$y <- d$x^2 - d$x + 1 + rnorm(d$x, sd = 0.1)
cuest <- locCuadSmootherC(d$x,d$y , xeval, bw, EpaK)
lpest2 <- locPolSmootherC(d$x,d$y , xeval, bw, 2, EpaK)
lpest3 <- locPolSmootherC(d$x,d$y , xeval, bw, 3, EpaK)
cbind(x = xeval, fx, cuad0 = cuest$beta0, lp20 = lpest2$beta0,
lp30 = lpest3$beta0, cuad1 = cuest$beta1, lp21 = lpest2$beta1,
lp31 = lpest3$beta1)
N <- 100
xeval <- 0:10/10
d <- data.frame(x = runif(N))
bw <- 0.125
fx <- xeval^2 - xeval + 1
##	Non random
d$y <- d$x^2 - d$x + 1
cuest <- locCuadSmootherC(d$x, d$y ,xeval, bw, EpaK)
lpest2 <- locPolSmootherC(d$x, d$y , xeval, bw, 2, EpaK)
print(cbind(x = xeval, fx, cuad0 = cuest$beta0,
lp0 = lpest2$beta0, cuad1 = cuest$beta1, lp1 = lpest2$beta1))
##	Random
d$y <- d$x^2 - d$x + 1 + rnorm(d$x, sd = 0.1)
cuest <- locCuadSmootherC(d$x,d$y , xeval, bw, EpaK)
lpest2 <- locPolSmootherC(d$x,d$y , xeval, bw, 2, EpaK)
lpest3 <- locPolSmootherC(d$x,d$y , xeval, bw, 3, EpaK)
cbind(x = xeval, fx, cuad0 = cuest$beta0, lp20 = lpest2$beta0,
lp30 = lpest3$beta0, cuad1 = cuest$beta1, lp21 = lpest2$beta1,
lp31 = lpest3$beta1)

Plugin Bandwidth selector.

Description

Implements a plugin bandwidth selector for the regression function.

Usage

pluginBw(x, y, deg, kernel, weig = rep(1,length(y)))
pluginBw(x, y, deg, kernel, weig = rep(1,length(y)))

Arguments

`x`	x covariate values.
`y`	y response values.
`deg`	degree of the local polynomial.
`kernel`	Kernel used to perform the estimation, see `Kernels`.
`weig`	Vector of weights for observations.

Details

Computes the plug-in bandwidth selector as shown in Fan and Gijbels(1996) book using pilots estimates as given in page 110-112 (Rule of thumb for bandwidth selection). Currently, only even values of p are can be used.

Value

A numeric value.

Note

Currently, only even values of p are can be used.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	cvBwSel <- regCVBwSelC(d$x,d$y, deg, kernel, interval = c(0, 0.25))
	thBwSel <- thumbBw(d$x, d$y, deg, kernel)
	piBwSel <- pluginBw(d$x, d$y, deg, kernel)
	est <- function(bw, dat, x) return(locPolSmootherC(dat$x,dat$y, x, bw, deg,
					kernel)$beta0)
	ise <- function(val, est) return(sum((val - est)^2 * xeval[[2]]))
	plot(d$x, d$y)
	trueVal <- regFun(xeval)
	lines(xeval, trueVal, col = "red")
	xevalRes <- est(cvBwSel, d, xeval)
	cvIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue")
	xevalRes <- est(thBwSel, d, xeval)
	thIse <- ise(trueVal, xevalRes)
	xevalRes <- est(piBwSel, d, xeval)
	piIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue", lty = "dashed")
	res <- rbind(	bw = c(cvBwSel, thBwSel, piBwSel),
					ise = c(cvIse, thIse, piIse) )
	colnames(res) <- c("CV", "th", "PI")
	res
size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	cvBwSel <- regCVBwSelC(d$x,d$y, deg, kernel, interval = c(0, 0.25))
	thBwSel <- thumbBw(d$x, d$y, deg, kernel)
	piBwSel <- pluginBw(d$x, d$y, deg, kernel)
	est <- function(bw, dat, x) return(locPolSmootherC(dat$x,dat$y, x, bw, deg,
					kernel)$beta0)
	ise <- function(val, est) return(sum((val - est)^2 * xeval[[2]]))
	plot(d$x, d$y)
	trueVal <- regFun(xeval)
	lines(xeval, trueVal, col = "red")
	xevalRes <- est(cvBwSel, d, xeval)
	cvIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue")
	xevalRes <- est(thBwSel, d, xeval)
	thIse <- ise(trueVal, xevalRes)
	xevalRes <- est(piBwSel, d, xeval)
	piIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue", lty = "dashed")
	res <- rbind(	bw = c(cvBwSel, thBwSel, piBwSel),
					ise = c(cvIse, thIse, piIse) )
	colnames(res) <- c("CV", "th", "PI")
	res

Parzen–Rosenblatt denstiy estimator.

Description

Parzen–Rosenblat univariate density estimator.

Usage

PRDenEstC(x, xeval, bw, kernel, weig = rep(1, length(x)))
PRDenEstC(x, xeval, bw, kernel, weig = rep(1, length(x)))

Arguments

`x`	vector with data points.
`xeval`	Vector of evaluation points.
`bw`	Smoothing parameter, bandwidth.
`kernel`	Kernel used to perform the estimation, see `Kernels`
`weig`	Vector of weights for observations.

Details

Simple Parzen–Rosenblat univariate density estimation, computed using definition.

Value

Returns an (x,den) data frame.

`x`	Evaluation points.
`den`	Density at each `x` point.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	N <- 100
	x <-  runif(N)
	xeval <- 0:10/10
	b0.125 <- PRDenEstC(x, xeval, 0.125, EpaK)
	b0.05 <- PRDenEstC(x, xeval, 0.05, EpaK)
	cbind(x = xeval, fx = 1, b0.125 = b0.125$den, b0.05 = b0.05$den)
N <- 100
	x <-  runif(N)
	xeval <- 0:10/10
	b0.125 <- PRDenEstC(x, xeval, 0.125, EpaK)
	b0.05 <- PRDenEstC(x, xeval, 0.05, EpaK)
	cbind(x = xeval, fx = 1, b0.125 = b0.125$den, b0.05 = b0.05$den)

Cross Validation Bandwidth selector.

Description

Implements Cross validation bandwidth selector for the regression function.

Usage

regCVBwSelC(x, y, deg, kernel=gaussK,weig=rep(1,length(y)),
interval=.lokestOptInt)
regCVBwSelC(x, y, deg, kernel=gaussK,weig=rep(1,length(y)),
interval=.lokestOptInt)

Arguments

`x`	x covariate values.
`y`	y response values.
`deg`	degree of the local polynomial.
`kernel`	Kernel used to perform the estimation, see `Kernels`.
`weig`	Vector of weights for observations.
`interval`	An interval where to look for the bandwidth.

Details

Computes the weighted ASE for every bandwidth returning the minimum. The function is implemented by means of a C function that computes for a single bandwidth the ASE, and a call to optimise on a given interval.

Value

A numeric value.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

H\"ardle W.(1990) Smoothing techniques. Springer Series in Statistics, New York (1991).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	cvBwSel <- regCVBwSelC(d$x,d$y, deg, kernel, interval = c(0, 0.25))
	thBwSel <- thumbBw(d$x, d$y, deg, kernel)
	piBwSel <- pluginBw(d$x, d$y, deg, kernel)
	est <- function(bw, dat, x) return(locPolSmootherC(dat$x,dat$y, x, bw, deg,
					kernel)$beta0)
	ise <- function(val, est) return(sum((val - est)^2 * xeval[[2]]))
	plot(d$x, d$y)
	trueVal <- regFun(xeval)
	lines(xeval, trueVal, col = "red")
	xevalRes <- est(cvBwSel, d, xeval)
	cvIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue")
	xevalRes <- est(thBwSel, d, xeval)
	thIse <- ise(trueVal, xevalRes)
	xevalRes <- est(piBwSel, d, xeval)
	piIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue", lty = "dashed")
	res <- rbind(	bw = c(cvBwSel, thBwSel, piBwSel),
					ise = c(cvIse, thIse, piIse) )
	colnames(res) <- c("CV", "th", "PI")
	res
size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	cvBwSel <- regCVBwSelC(d$x,d$y, deg, kernel, interval = c(0, 0.25))
	thBwSel <- thumbBw(d$x, d$y, deg, kernel)
	piBwSel <- pluginBw(d$x, d$y, deg, kernel)
	est <- function(bw, dat, x) return(locPolSmootherC(dat$x,dat$y, x, bw, deg,
					kernel)$beta0)
	ise <- function(val, est) return(sum((val - est)^2 * xeval[[2]]))
	plot(d$x, d$y)
	trueVal <- regFun(xeval)
	lines(xeval, trueVal, col = "red")
	xevalRes <- est(cvBwSel, d, xeval)
	cvIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue")
	xevalRes <- est(thBwSel, d, xeval)
	thIse <- ise(trueVal, xevalRes)
	xevalRes <- est(piBwSel, d, xeval)
	piIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue", lty = "dashed")
	res <- rbind(	bw = c(cvBwSel, thBwSel, piBwSel),
					ise = c(cvIse, thIse, piIse) )
	colnames(res) <- c("CV", "th", "PI")
	res

Kernel selection.

Description

Uses kernel attributes to selects kernels. This function is mainly used for internal purposes.

Usage

selKernel(kernel)
selKernel(kernel)

Arguments

kernel

kernel to use.

Details

Uses RK(K) to identify a kernel. The integer is used in the C code part to perform computations with given kernel. It allows for a kernel selection in C routines. It is used only for internal purposes.

Value

An integer that is unique for each kernel.

Warning

Used only for internal purposes.

Author(s)

Jorge Luis Ojeda Cabrera.

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

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.

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.

Value

Both functions returns a data.frame with

`x`	$x$ evaluation points.
`reg`	the smoothed values at `x` points.

...

Author(s)

Jorge Luis Ojeda Cabrera.

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) ) )
	}
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) ) )
	}

Rule of thumb for bandwidth selection.

Description

Implements Fan and Gijbels(1996)'s Rule of thumb for bandwidth selection

Usage

thumbBw(x, y, deg, kernel, weig = rep(1, length(y)))
compDerEst(x, y, p, weig = rep(1, length(y)))
thumbBw(x, y, deg, kernel, weig = rep(1, length(y)))
compDerEst(x, y, p, weig = rep(1, length(y)))

Arguments

`x`	x covariate data values.
`y`	y response data values.
`p`	order of local polynomial estimator.
`deg`	Local polynomial estimation degree($p$).
`kernel`	Kernel used to perform the estimation.
`weig`	weights if they are required.

Details

See Fan and Gijbels(1996) book, Section 4.2. This implementation is also considering weights. compDerEst computes the $p+1$ derivative of the regression function in a simple manner, assuming it is a polynomial in $x$ . thumbBw gives a bandwidth selector by means of pilot estimator given by compDerEst and the mean of residuals.

Value

thumbBw returns a single numeric value, while compDerEst returns a data frame whose components are:

`x`	x values.
`y`	y values.
`res`	residuals for the parametric estimation.
`der`	derivative estimation at x values.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

Examples

	size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	cvBwSel <- regCVBwSelC(d$x,d$y, deg, kernel, interval = c(0, 0.25))
	thBwSel <- thumbBw(d$x, d$y, deg, kernel)
	piBwSel <- pluginBw(d$x, d$y, deg, kernel)
	est <- function(bw, dat, x) return(locPolSmootherC(dat$x,dat$y, x, bw, deg,
					kernel)$beta0)
	ise <- function(val, est) return(sum((val - est)^2 * xeval[[2]]))
	plot(d$x, d$y)
	trueVal <- regFun(xeval)
	lines(xeval, trueVal, col = "red")
	xevalRes <- est(cvBwSel, d, xeval)
	cvIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue")
	xevalRes <- est(thBwSel, d, xeval)
	thIse <- ise(trueVal, xevalRes)
	xevalRes <- est(piBwSel, d, xeval)
	piIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue", lty = "dashed")
	res <- rbind(	bw = c(cvBwSel, thBwSel, piBwSel),
					ise = c(cvIse, thIse, piIse) )
	colnames(res) <- c("CV", "th", "PI")
	res
size <- 200
	sigma <- 0.25
	deg <- 1
	kernel <- EpaK
	xeval <- 0:100/100
	regFun <- function(x) x^3
	x <- runif(size)
	y <- regFun(x) + rnorm(x, sd = sigma)
	d <- data.frame(x, y)
	cvBwSel <- regCVBwSelC(d$x,d$y, deg, kernel, interval = c(0, 0.25))
	thBwSel <- thumbBw(d$x, d$y, deg, kernel)
	piBwSel <- pluginBw(d$x, d$y, deg, kernel)
	est <- function(bw, dat, x) return(locPolSmootherC(dat$x,dat$y, x, bw, deg,
					kernel)$beta0)
	ise <- function(val, est) return(sum((val - est)^2 * xeval[[2]]))
	plot(d$x, d$y)
	trueVal <- regFun(xeval)
	lines(xeval, trueVal, col = "red")
	xevalRes <- est(cvBwSel, d, xeval)
	cvIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue")
	xevalRes <- est(thBwSel, d, xeval)
	thIse <- ise(trueVal, xevalRes)
	xevalRes <- est(piBwSel, d, xeval)
	piIse <- ise(trueVal, xevalRes)
	lines(xeval, xevalRes, col = "blue", lty = "dashed")
	res <- rbind(	bw = c(cvBwSel, thBwSel, piBwSel),
					ise = c(cvIse, thIse, piIse) )
	colnames(res) <- c("CV", "th", "PI")
	res

Package 'locpol'

Help Index

Bivariate Local estimation.

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Compute kernel values.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

CV bandwidth selector for density

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Equivalent Kernel.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Kernel characteristics

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Kernel Constants used in Bandwidth Selection.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Kernels.

Description

Usage

Arguments

Details

Author(s)

References

See Also

Local Polynomial Weights

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Local Polynomial estimation.

Description

Usage