Hi R experts,

I'm trying to use nls() for a piecewise linear regression with the first
slope constrained to 0. There are 10 data points and when it does converge
the second slope is almost always over estimated for some reason. I have
many sets of these 10-point datasets that I need to do. The following
segment of code is an example, and sorry for the overly precise numbers,
they are just copied from real data.

y1<-c(2.37700445, 1.76209775, 0.09795576, 2.21834963, 6.62262243,
15.70471269,  21.92956392, 36.39401717, 32.43620195, 44.77442277)
x1<-c(24.6, 28.9, 33.2, 37.6, 42.0, 46.4, 50.9, 55.3, 59.8, 64.3)

dat <- data.frame(x1,y1)
nlmod <- nls(y1 ~ ifelse(x1 < xint+(yint/slp), yint, yint +
(x1-(xint+(yint/slp)))*slp),
            data=dat, control=list(minFactor=1e-5,maxiter=500,warnOnly=T),
            start=list(xint=39.27464924, yint=0.09795576, slp=2.15061064),
            na.action=na.omit, trace=T)

##plotting the function
plot(dat$x1,dat$y1)
segments(x0=0, x1=coef(nlmod)[1]+coef(nlmod)[2]*coef(nlmod)[3],
            y0=coef(nlmod)[2], y1=coef(nlmod)[2])
segments(x0=coef(nlmod)[1]+coef(nlmod)[2]*coef(nlmod)[3],x1=80,
            y0=coef(nlmod)[2], y1=80*coef(nlmod)[3]+coef(nlmod)[2])

As you can see from the plot, the line is above all data points on the
second segment. This seems to be the case for different datasets. I'm
wondering if anyone can help me understand why this happens. Is this because
there are too few data points or is it because the likelihood function is
just not smooth enough?

Karen

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