| pcf.kernesti | R Documentation |
Computes the values of a multivariate kernel regression estimator (Nadaraya-Watson estimator) on a regular grid.
pcf.kernesti(x, y, h, N, kernel="gauss", support=NULL)
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"; in the multivariate case can also be "bart" |
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) |
a piecewise constant function
Jussi Klemela
kernesti.regr,
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.kernesti(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)