regpro (version 0.1.1)

kernesti.der: An estimator of a partial derivative of a regression function at one point

Description

Computes the value of a multivariate kernel estimator of a partial derivative of a regression function at a one point.

Usage

kernesti.der(arg, x, y, h=1, direc=1, kernel="gauss", vect=FALSE)

Arguments

arg

d-vector; the point where the estimate is evaluated

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

direc

integer 1,...,d; indicates which partial derivative is estimated

kernel

a character; determines the kernel function; can only be "gauss"

vect

TRUE or FALSE; an internal parameter related to the method of calculation

Value

See Also

pcf.kernesti.der,

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)

arg<-c(0,0)
kernesti.der(arg,x,y,h=0.5)