Multivariate local linear estimator

Description

Computes the values of a multivariate local linear regression estimator on a regular grid.

Usage

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pcf.loclin(x, y, h, N, type=0, kernel="gauss", support=NULL, alt=FALSE, alt2=FALSE)

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

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

Value

a piecewise constant function

Author(s)

Jussi Klemela

See Also

pcf.kernesti,

Examples

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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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