> A quick look at the code for Siegel in mblm reveals that it is extremely

> inefficient, but it seems to be correct.

> One “explanation” for this behavior, presuming that we haven’t overlooked

> something more basic, is that such

> high breakdown estimates sacrifice some efficiency, that is to say, they

> are more variable than other methods

> when the data is well behaved, and of course, the Galton data is famously

> “almost Gaussian”.

>

> On Feb 11, 2019, at 12:47 PM, Marco Besozzi <

[hidden email]>

> wrote:

>

> Thank you very much for your reply.

> If I have well understood, unfortunately in this way I have lost the only

> idea I had...

> Do you believe that a problem in the R algorithm employed in the package

> mblm for Siegel regression is possible?

> And do you know if Siegel regression is available in a different package?

> I was unable to find it.

> Thanks again!

> Best regards.

>

> P.S.: sorry for my bad english...

>

> Il giorno lun 11 feb 2019 alle ore 12:54 Roger Koenker <

>

[hidden email]> ha scritto:

>

>> My first thought was also that this was an artifact of the ties, but

>> dithering the data

>> n <- length(child)

>> child <- child + runif(n,-.5,.5)

>> parent <- parent + runif(n,-.5,.5)

>>

>> and rerunning yields the same discrepancy between the Siegel and other

>> fits. Curiously, both

>> lmsreg and ltsreg from MASS produce lines that are more steeply sloped

>> than those

>> of the other methods. Since I stupidly forgot to set.seed(), YMMV.

>>

>> > On Feb 11, 2019, at 10:24 AM, Marco Besozzi <

[hidden email]>

>> wrote:

>> >

>> > I employed the "galton" set of data included in the package "psych".

>> With

>> > the package "mblm" I obtained the Theil-Sen nonparametric regression and

>> > the Siegel non parametric regression, and compared them with the

>> ordinary

>> > least square regression line.

>> > The results of standard regression and Theil-Sen regression are

>> practically

>> > identical. But the Siegel regression seems to have a bias that I cannot

>> > understand. May I ask for a possible explanation? The bias may be

>> related

>> > to the number of ties in the set of data? Here's the code and the image.

>> >

>> > Best regards.

>> >

>> > Marco Besozzi

>> > # Theil-Sen and Siegel nonparametric regression with package mblm

>> > # comparison with ordinary least squares (parametric) regression

>> > # on galton set of data included in the package psych

>> > #

>> > library(psych)

>> > attach(galton)

>> > library(mblm)

>> > #

>> > reglin_yx <- lm(child ~ parent, data=galton) # ordinary least squares

>> > (parametric) regression

>> > a_yx <- reglin_yx$coefficients[1] # intercept a

>> > b_yx <- reglin_yx$coefficients[2] # slope b

>> > #

>> > regnonTS <- mblm(child ~ parent, data=galton, repeated=FALSE) #

>> Theil-Sen

>> > nonparametric regression (wait a few minutes!)

>> > a_TS <- regnonTS$coefficients[1] # intercept a

>> > b_TS <- regnonTS$coefficients[2] # slope b

>> > #

>> > regnonS = mblm(child ~ parent, data=galton, repeated=TRUE) # Siegel

>> > nonparametric regression

>> > a_S <- regnonS$coefficients[1] # intercept a

>> > b_S <- regnonS$coefficients[2] # slope b

>> > #

>> > # xy plot of data and regression lines

>> > #

>> > windows() # open a new window

>> > plot(parent, child, xlim = c(60,80), ylim = c(60,80), pch=1,

>> xlab="Parent

>> > heigt (inch)", ylab="Chile height (inch)", main="Regression lines

>> > comparison", cex.main = 0.9) # data plot

>> > abline(a_yx, b_yx, col="green", lty=1) # ordinary least squares

>> > (parametric) regression line

>> > abline(a_TS, b_TS, col="blue", lty=1) # Theil-Sen nonparametric

>> regression

>> > line

>> > abline(a_S, b_S, col="red", lty=1) # Siegel nonparametric regression

>> > legend(60, 80, legend=c("Ordinary least squares regression", "Theil-Sen

>> > nonparametric regression","Siegel nonparametric regression"),

>> > col=c("green", "blue", "red"), lty=c(4,4,1), cex=0.8) # add a legend

>> > #

>> > <Siegel.PNG>______________________________________________

>> >

[hidden email] mailing list -- To UNSUBSCRIBE and more, see

>> >

https://stat.ethz.ch/mailman/listinfo/r-help>> > PLEASE do read the posting guide

>>

http://www.R-project.org/posting-guide.html>> <

http://www.r-project.org/posting-guide.html>

>> > and provide commented, minimal, self-contained, reproducible code.

>>

>>

>