An additive model regression estimator for pointwise estimation

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Description

Computes the value of a regression function estimator at one point, when the estimator is based on the additive model.

Usage

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additive(x, y, arg=NULL, eval=NULL, h=1, kernel="gauss", M=2, vect=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

arg

d-vector; the point where the estimate is evaluated

eval

either NULL or a n*d matrix; the matrix that gives the evaluations of the coordinate functions at the data points

h

a positive real number; the smoothing parameter of the kernel estimate

kernel

a character; determines the kernel function; either "gauss" or "uniform"

M

integer >=2; the number of iterations

vect

TRUE or FALSE; internal parameter

Value

list of eval, value, and valvec; "eval" is a n*d matrix of the evaluations of the estimated component functions at the data points; "value" is a real number giving the estimated value of the regression function at one point; "valvec" is d vector giving the estimated values of the component functions at one point

Author(s)

Jussi Klemela

See Also

pcf.additive,

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)

additive(x,y)

arg<-c(0,0)
additive(x,y,arg=arg)

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