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: plm
See also: plmk plmlorg

Quantlet: plmp
Description: plmp estimates the parametric part in partially linear models by using piecewise polynomial to approximate the nonparametric part

Usage: res = plmp(x,t,y,m,mn)
Input:
x n x p matrix, the design
t n x 1 matrix, the design in [0, 1]
y n x 1 matrix, the response
m scalar, degree of piecewise polynomial
mn scalar, the numbers of intervals divided [0, 1] with length 1/mn.
Output:
res.hbeta p x 1 matrix, the estimate of the parameter
res.hg n x 1 matrix, estimate of nonparameter function

Example:
library("plm")
n = 100
sig=0*matrix(3,3)
sig[,1]=#(0.81,0.1,0.2)
sig[,2]=#(0.1,2.25,0.1)
sig[,3]=#(0.2,0.1,1)
x =normal(n,3)*sig
t =sort(uniform(n))
beta0=#(1.2, 1.3, 1.4)
y =x*beta0+t^3+0.01*normal(n)
m =2
mn=5
res=plmp(x,t,y,m,mn)
ddp=createdisplay(1,1)
datah1=t~t^3
datah2=t~res.hg
part=grid(1,1,rows(t))'
setmaskp(datah1,1,0,1)
setmaskp(datah2,4,0,3)
setmaskl(datah1,part,1,1,1)
setmaskl(datah2,part,4,1,3)
show(ddp,1,1,datah1,datah2)
setgopt(ddp,1,1,"xlabel","T","title","Simulation comparison","ylabel","g(T) and its estimate values")

Result:
The parameter estimates, see Hung Chen "Convergence
Rates for Parametric Components in A Partly Liner Model",
Ann. of Statist. (1988) 16, 136-146.



Author: H. Liang, W. Haerdle, 19980512 license MD*Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006