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

 Quantlet: plmk Description: plmk estimates the parameter part in partially linear models by using kernel to approximate the nonparametric part

 Usage: res = plmk(x,t,y,h) 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 h p x 1 matrix or scalar, chosen bandwidth Output: res.hbeta p x 1 matrix, estimate of parameter res.hsigma scalar, estimate of variance 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)  ; the true value
y =x*beta0+t^3+0.01*normal(n)
h =0.5
res=plmk(x,t,y,h)
res.hbeta               ; the estimate of beta
res.hsigma              ; the estimate of the variance when error is homoscedastic
ddp=createdisplay(1,1)
datah1=t~t^3
datah2=t~res.hg
part=grid(1,1,rows(t))'
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 Jiti Gao, Shengyan Hong and
Hua Liang" Convergence rate in partly linear models",
Acta Mathematical Sinica (1995) 17, 170-180.
```

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