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 plmp

Quantlet: plmls
Description: plmls estimates partially linear models by using least squares spline to approximate the nonparametric part.

Usage: res = plmls(x,t,y,m,knots)
Input:
x n x p matrix, the design points
t n x 1 matrix, the design ponits
y n x 1 matrix, the response variables
m the order of spline
knots k x 1 matrix, knot sequence knots
Output:
res.hbeta p x 1 matrix, the estimate of the parameter
res.hg n x 1 matrix, the estimate of the nonparametric part

Example:

library("plm")

proc()=main()

  n = 100

  m=2

  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)

  kk=floor(rows(t)/2)

  knots=0*(1:kk)

  j=1

  while(j<=kk)

    knots[j]=t[2*j]

    j=j+1

  endo

  res=plmls(x,t,y,m,knots)

  ddls=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(ddls,1,1,datah1,datah2)

  setgopt(ddls,1,1,"xlabel","T","title","Simulation comparison","ylabel","g(T) and its estimate values")

Result:

The parameter estimates, 3 x 1 matrix and nonparametric 

fitting are presented.


Library: plm
See also: plmk plmp

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, 98
(C) MD*TECH Method and Data Technologies, 21.9.2000