Still OT... but here is my own (I think previously mentioned here) rant on
people thrashing about with log transformation and an all-too-common kludge to
deal with zeros mixed among small numbers...
https://gist.github.com/jdnewmil/99301a88de702ad2fcbaef33326b08b4
OP perhaps posting a link
In the spirit of Martin's comments, it is perhaps worthwhile to note one of
John Tukey's (who I actually knew) pertinent quotes:
"The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
Ah LOD's, typically LLOD's ("lower limits of detection").
Disclaimer: I am *NOT* in any sense an expert on such matters. What follows
are just some comments based on my personal experience. Please filter
accordingly. Also, while I kept it on list as Martin suggested it might be
useful to do
Dear ESS users,
I�m happy to report that we now have a new release. The first since
2018. It is available on github at https://github.com/emacs-ess/ESS
and ELPA (see below). Although an official release, in many ways
this is/was an experiment. I had never done a release before and
I really
Dear Martin,
Helpful general advice, although it's perhaps worth mentioning that the
geometric mean, defined e.g. naively as prod(x)^(1/length(x)), is
necessarily 0 if there are any 0 values in x. That is, the geometric
mean "works" in this case but isn't really informative.
Best,
John
--
On Mon, 22 Jan 2024, Martin Maechler wrote:
I think it is a good question, not really only about geo-chemistry, but
about statistics in applied sciences (and engineering for that matter).
John W Tukey (and several other of the grands of the time) had the log
transform among the "First aid
> Rich Shepard
> on Mon, 22 Jan 2024 07:45:31 -0800 (PST) writes:
> A statistical question, not specific to R. I'm asking for
> a pointer for a source of definitive descriptions of what
> types of data are best summarized by the arithmetic,
> geometric, and harmonic
On Mon, 22 Jan 2024, Ben Bolker wrote:
I think https://stats.stackexchange.com would be best: r-sig-ecology is
pretty quiet these days
Okay, Ben.
Thanks,
Rich
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R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
I think https://stats.stackexchange.com would be best: r-sig-ecology
is pretty quiet these days
On 2024-01-22 11:05 a.m., Rich Shepard wrote:
On Mon, 22 Jan 2024, Bert Gunter wrote:
better posted on r-sig-ecology? -- or maybe even stack exchange?
Bert,
Okay.
Regards,
Rich
On Mon, 22 Jan 2024, Bert Gunter wrote:
better posted on r-sig-ecology? -- or maybe even stack exchange?
Bert,
Okay.
Regards,
Rich
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https://stat.ethz.ch/mailman/listinfo/r-help
better posted on r-sig-ecology? -- or maybe even stack exchange?
Cheers,
Bert
On Mon, Jan 22, 2024 at 7:45 AM Rich Shepard
wrote:
> A statistical question, not specific to R.
>
> I'm asking for a pointer for a source of definitive descriptions of what
> types of data are best summarized by the
A statistical question, not specific to R.
I'm asking for a pointer for a source of definitive descriptions of what
types of data are best summarized by the arithmetic, geometric, and harmonic
means.
As an aquatic ecologist I see regulators apply the geometric mean to
geochemical concentrations
Dear all,
After being archived on CRAN on 2023-10-16 , hydroGOF is finally back on
CRAN since January 21th: https://cran.r-project.org/package=hydroGOF.
This new version 0.5-4 includes:
*) the following new functions:
-) KGElf (García et al., 2017),
-) sKGE (Fowler et al., 2018),
Dear all,
After being archived on CRAN on 2023-10-1, hydroTSM is finally back on CRAN
since January 18th: https://cran.r-project.org/package=hydroTSM.
This new version 0.7-0 has several new functions, improvements, bugfixes,
and a new dataset, mostly devoted to work with sub-daily and sub-hourly
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