Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: gam
See also: intest

Macro: pcad
Description: pcad estimates the additive components, the significant directions and the regression on principal components

Usage: {jhat,g,mhat} = pcad(x,xg,y,h,bn)
Input:
x n x d matrix, the design
xg ng x d matrix, the points where we want to estimate
y n x 1 matrix, the response
h d x 1 matrix or scalar, chosen bandwidth
bn scalar, threshold for choosing significant directions
Output:
jhat q x 1 matrix, the set of significant directions
g ng x q matrix, additive functions on principal components
mhat ng x 1 matrix, estimate of regression using the significant functions

Example:
library("gam")
n = 100
v =uniform(n,4)
x =v[,2:4]
y =x[,1]^2+0.1*x[,2]+normal(n)
h =0.5
bn=0.02 
gest=pcad(x,x,y,h,bn)
gest.jhat
gest.g
gest.mhat
Result:
The significant directions and the significant
function, see Haerdle and Tsybakov "Additive
Nonparametric Regression on Principal Components",
J. Nonparametric Statist. (1994)157-84.

Library: gam
See also: intest

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Hua Liang, Stefan Sperlich 961112
(C) MD*TECH Method and Data Technologies, 28.6.1999