Sorry for my delay..
If you do not know the breakpoint, I would suggest to estimate it..
Have a look to the segmented package. The relevant code here is
attach(d)
m0<-lm(percent~ year , weights=1/se)
library(segmented)
mseg<-segmented(m0,seg.Z=~year,psi=1995)
points(year, fitted(mseg))
Hope this helps you,
regards,
vito
David Winsemius ha scritto:
It actually looked reasonably economical but the output certainly is
ugly. I see a variety of approaches in the r-help archives. This thread
discusses two other approaches, degree-one splines from Berry and hard
coded-coefficients from Lumley:
http://finzi.psych.upenn.edu/R/Rhelp08/archive/118046.html
The Lumley solution has the advantage which he articulates that the
slopes are more directly interpretabel and in this case you can see that
yourversion's year slope agrees with Lumley's suggested parametrization:
> m=lm(percent~ year + pmax(year,1996) + pmin(year, 1996), weights=1/se,
+ subset=year>=1988, da=d);
> m
Call:
lm(formula = percent ~ year + pmax(year, 1996) + pmin(year, 1996),
data = d, subset = year >= 1988, weights = 1/se)
Coefficients:
(Intercept) year pmax(year, 1996) pmin(year, 1996)
1161.3126 -0.2177 -0.3494 NA
More compact output to boot.
--
====================================
Vito M.R. Muggeo
Dip.to Sc Statist e Matem `Vianelli'
Università di Palermo
viale delle Scienze, edificio 13
90128 Palermo - ITALY
tel: 091 6626240
fax: 091 485726/485612
http://dssm.unipa.it/vmuggeo
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