| additive.stage | R Documentation |
Computes the value of an additive model regression estimator at one point using stagewise fitting
additive.stage(x, y=NULL, arg=NULL, residu=NULL, deet=NULL, h=1, kernel="gauss", M=2, vect=FALSE)
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 |
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
Jussi Klemela
pcf.additive,
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)