An estimator of a marginal average of a regression function

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Description

Computes the values of an estimator of a marginal average of a regression function on a regular grid. Marginal averages are called also partial dependency functions.

Usage

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pcf.kernesti.marg(x, y, h, N, kernel="gauss", coordi=1)

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; the only allowed value is "gauss"

coordi

integer 1,...,d; indicates which marginal average is calculated

Value

a piecewise constant function

Author(s)

Jussi Klemela

See Also

pcf.kernesti,

Examples

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n<-10
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.marg(x,y,h=0.5,N=num)

dp<-draw.pcf(pcf,minval=min(y))
plot(dp$x,dp$y,type="l")

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