2. With this hint and the posting guide "http://www.R-project.org/posting-guide.html";, you may find more information. A search there exposed much discussion of "robust regression" and even "robust nonlinear regression", if you actually still need that. In addition, I found useful information on robust regression in Venables and Ripley (2002) Modern Applied Statistics with S, 4th ed. (Springer).
hope this helps. spencer graves
cstrato wrote:
Dear all
Here is a hopefully better example with regards to nonlinear robust fitting:
# fitting a polynomial: x <- seq(-10,10,0.2) y <- 10*x + 4*x*x - 2*x*x*x plot(x,y) z <- jitter(y,amount=300) plot(x,z) df <- as.data.frame(cbind(x,z)) nf <- nls(z ~ a*x + b*x*x + c*x*x*x, data=df, + start=list(a=4,b=2,c=1), trace = TRUE) 127697531 : 4 2 1 2974480 : 10.972123 3.793426 -1.942278
# introducing outliers before fitting the polynomial: z1 <- z z1[c(16,22,23,34,36,42,67,69,72,76)] <- + c(2000,1900,2000,1900,1600,1600,500,-2000,-1700,-1800) plot(x,z1) df1 <- as.data.frame(cbind(x,z1)) nf1 <- nls(z1 ~ a*x + b*x*x + c*x*x*x, data=df1, + start=list(a=4,b=2,c=1), trace = TRUE) 159359174 : 4 2 1 24098548 : -59.053288 4.169518 -1.072027
# plotting the results: y1 <- 10.97*x + 3.79*x*x - 1.94*x*x*x y2 <- -59.05*x + 4.17*x*x - 1.07*x*x*x oldpar <- par(pty="s",mfrow=c(2,2),mar=c(5,5,4,1)) plot(x,y) plot(x,z1) plot(x,y1) plot(x,y2) par(oldpar)
In my opinion this fit could hardly be considered to be robust.
Are there functions in R which can do robust fitting? (Sorrowly, at the moment I could not test the package nlrq mentioned by Roger Koenker)
Best regards Christian _._._._._._._._._._._._._._._._ C.h.i.s.t.i.a.n S.t.r.a.t.o.w.a V.i.e.n.n.a A.u.s.t.r.i.a _._._._._._._._._._._._._._._._
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