## An additive model regression estimator for pointwise estimation

### Description

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

### Usage

```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

`pcf.additive`,

### 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)