On Mon, 30 May 2005 19:38:58 -0400 (EDT) Abhyuday Mandal wrote: > 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 > .186,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202) > > there are two change points. so the fitted curve should look like > > > > \ > \ /\ > \/ \ > \ > \ > > How do I do this in R ?
There are various ways, as the previous replies already indicated. One approach would be to consider this in a structural change framework, as implemented in strucchange or segmented. With strucchange you can do ## collect data dat <- data.frame(y, x) ## add small noise set.seed(123) dat$x <- dat$x + rnorm(21, sd = 0.001) ## fit breakpoint model bp <- breakpoints(y ~ x, data = dat) ## visualize plot(x, y) lines(x, fitted(bp)) The problem with breakpoints() is that it also fits models on very small subsets, which can lead to singular regressor matrices if there are bindings among the regressors. Hence, I did an ugly hack and added a small error to avoid singularities. segmented can also fit segmented regressions, but always fits broken line segments (i.e. continuous curves) whereas strucchange fits fully segmented models. The latter can also be easily fitted via lm(y ~ fac/x) if fac is a factor coding the different segments. See also ?breakfactor in strucchange. So you can use any segmentation algorithm and then plug the result easily into lm(). Z > Thank you, > Abhyuday > > ______________________________________________ > R-help@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 > ______________________________________________ R-help@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
