Description

Computes the value of an additive model regression estimator at one point using stagewise fitting

Usage

 ```1 2``` ```additive.stage(x, y=NULL, arg=NULL, residu=NULL, deet=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 or NULL; the values of the response variable; if "residu" is given, then "y" is not used `arg` d-vector; the point where the estimate is evaluated `residu` NULL or n*M matrix of residuals; for each step the n vector of residuals is given; the first residual is the vector of y-observations `deet` NULL or M vector of values 1,...,d; for each step the direction chosen by the optimizer `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, residu, deet, value, and valvec; "eval" is a n*d matrix of the evaluations of the estimated component functions at the data points; "residu" is n*M matrix which contains the sequence of estimates evaluated at the observations; "deet" is M vector of values 1,...,d which indicates for each step the direction chose by the optimization procedure; "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`,
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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) as<-additive.stage(x,y) arg<-c(0,0) additive.stage(x,arg=arg,residu=as\$residu,deet=as\$deet) ```