Description Usage Arguments Value Author(s) See Also Examples
Computes the values of a multivariate local linear regression estimator on a regular grid.
1 |
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 |
type |
integer 0,...,d; if type=0, then the regression function is estimated, otherwise the first partial derivative of the variable indicated by type is estimated |
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) |
alt |
an internal parameter |
alt2 |
an internal parameter |
a piecewise constant function
Jussi Klemela
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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.loclin(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)
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Loading required package: denpro
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