Incidentally (though I don't expect anyone will want to pursue it)
Johnstone & Velleman give standard errors for the estimates in Johnstone,
Iain M., and Paul F. Velleman. “The Resistant Line and Related Regression
Methods.” Journal of the American Statistical Association, vol. 80, no.
392, 1985, p
Sorry, I have seen it too late that we had different tab width in the original
file and my editor.
Here is the patch with all white spaces instead of mixing tabs and white spaces.
Serguei.
Le 29/05/2017 à 15:13, Serguei Sokol a écrit :
Here is an attached patch.
Best,
Serguei.
Le 29/05/2017
Here is an attached patch.
Best,
Serguei.
Le 29/05/2017 à 12:21, Serguei Sokol a écrit :
The problem or actual R implementation relies on an assumption
that median(x[i] | x[i] <= quantile(x, 1/3)) == quantile(x, 1/6)
which reveals not to be true despite very trustful appearance.
If we continue
On 05/18/2017 06:54 PM, Joy wrote:
Sorry, this might be a really basic question, but I'm trying to interpret
the results from memory profiling, and I have a few questions (marked by
*Q#*).
From the summaryRprof() documentation, it seems that the four columns of
statistics that are reported when
The problem or actual R implementation relies on an assumption
that median(x[i] | x[i] <= quantile(x, 1/3)) == quantile(x, 1/6)
which reveals not to be true despite very trustful appearance.
If we continue with the example of x=y=1:9
then quantile(x, 1/6)=2.5 (here quantile() is taken in C-code s
A usually trustworthy R correspondent posted a pure R implementation on SO at
some point in his lost youth:
https://stackoverflow.com/questions/3224731/john-tukey-median-median-or-resistant-line-statistical-test-for-r-and-line
This one does indeed generate the line of identity for the (1:9, 1:9)
