Karen Chang Liu
2010-Apr-19 15:32 UTC
[R] nls for piecewise linear regression not converging to least square
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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Gabor Grothendieck
2010-Apr-19 19:36 UTC
[R] nls for piecewise linear regression not converging to least square
Try reparameterizing:
nlmod2 <- nls(y2 ~ pmax(1/p, (x2 - xint)), data = dat,
start = list(xint = 40.49782, p = 1), trace = TRUE, alg =
"plinear")
On Mon, Apr 19, 2010 at 11:32 AM, Karen Chang Liu <karencl at uw.edu>
wrote:> 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
>
> ? ? ? ?[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
Karen Liu
2010-Apr-19 19:37 UTC
[R] nls for piecewise linear regression not converging to least square
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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Thomas Lumley
2010-Apr-19 21:40 UTC
[R] nls for piecewise linear regression not converging to least square
On Mon, 19 Apr 2010, Karen Chang Liu wrote:> 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? >I think there's something wrong with your graph. If I do points(x1,fitted(nlmod),col="red") I get points that are on the horizontal line segment, but then go through the data nicely on the right. -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.edu University of Washington, Seattle