Piecewise Linear Regression

It is conventional to fit piecewise linear models by assuming  
Gaussian error and
using least squares methods, but one can argue that median regression  
provides
a more robust approach to this problem.  You might consider the  
following fit:

  x = c 
(6.25,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,5 
0.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00)
  y = c 
(0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.186 
,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202)
library(quantreg)
plot(x,y)
fit <- rqss(y ~ qss(x))
plot(fit)

it gives 5 segments not 3, but this can be controlled by the choice  
of lambda in the qss
function, for example, try:

fit <- rqss(y ~ qss(x,lambda=3)
plot(fit,col="red")

which gives a fit like you suggest might be reasonable with only  
three segments.



url:    www.econ.uiuc.edu/~roger                Roger Koenker
email   rkoenker at uiuc.edu                       Department of Economics
vox:    217-333-4558                            University of Illinois
fax:    217-244-6678                            Champaign, IL 61820

Hi,

I need to fit a piecewise linear regression.

x = c 
(6.25,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50 
,50.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00)
y = c 
(0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.1 
86,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202)

there are two change points. so the fitted curve should look like



\
 \  /\
  \/  \
       \
        \

How do I do this in R ?

Thank you,
Abhyuday

______________________________________________
R-help at stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting- 
guide.html