regpro (version 0.1.1)

pcf.additive: Regression function estimator in the additive model

Description

Computes the values of an additive model regression estimator on a regular grid.

Usage

pcf.additive(x, y, h, N, kernel="gauss", support=NULL, M=2, eval=NULL, direc=NULL)

Arguments

x

n*d data matrix; the matrix of the values of the explanatory variables

y

n vector; the values of the response variable

h

a positive real number; the smoothing parameter of the kernel estimate

N

vector of d positive integers; the number of grid points for each direction

kernel

a character; determines the kernel function; either "gauss" or "uniform"

support

either NULL or a 2*d vector; the vector gives the d intervals of a rectangular support in the form c(low_1,upp_1,...,low_d,upp_d)

M

integer >=2; the number of iterations

eval

either NULL or a n*d matrix; the matrix that gives the evaluations of the coordinate functions at the data points

direc

either NULL or an integer 1,...,d; if direc is NULL, then the complete regression function estimator is estimated, otherwise only the component indicated by "direc" is estimated

Value

See Also

additive,

Examples

set.seed(1)
n<-100
d<-2 
x<-8*matrix(runif(n*d),n,d)-3
C<-(2*pi)^(-d/2)
phi<-function(x){ return( C*exp(-sum(x^2)/2) ) }
D<-3; c1<-c(0,0); c2<-D*c(1,0); c3<-D*c(1/2,sqrt(3)/2)
func<-function(x){phi(x-c1)+phi(x-c2)+phi(x-c3)}
y<-matrix(0,n,1)
for (i in 1:n) y[i]<-func(x[i,])+0.01*rnorm(1)

num<-30  # number of grid points in one direction
pcf<-pcf.additive(x,y,h=0.5,N=c(num,num))

dp<-draw.pcf(pcf,minval=min(y))
persp(dp$x,dp$y,dp$z,phi=30,theta=-30)
contour(dp$x,dp$y,dp$z,nlevels=30)