Dear Martin,

On 7/28/2020 10:17 AM, Martin Maechler wrote:
Martin Maechler
     on Tue, 28 Jul 2020 15:56:10 +0200 writes:

John Fox
     on Mon, 27 Jul 2020 12:57:57 -0400 writes:

     >> Dear Dileepkumar R,
     >> As is obvious from the tick marks, the vertical axis is not log-scaled:

     >>> log10(99.999) - log10(99.99)
     >> [1] 3.908865e-05
     >>> log10(99) - log10(90)
     >> [1] 0.04139269


     >> That is, these (approximately?) equally spaced ticks aren't equally
     >> spaced on the log scale.

     >> The axis is instead apparently (at least approximately) on the logit
     >> (log-odds) scale:

     >>> library(car)
     >> Loading required package: carData
     >>> logit(99.999) - logit(99.99)
     >> [1] 2.302675
     >>> logit(99) - logit(90)
     >> [1] 2.397895

     > Small remark : You don't need car (or any other extra pkg) to have logit:

     > logit <- plogis # is sufficient

     > Note that the ?plogis (i.e. 'Logistic') help page has had a
     > \concept{logit}

     > entry (which would help if one used  help.search() .. {I don't;
     > I have 10000 of packages}),
     > and that same help page has been talking about 'logit' for ca 16
     > years now (and I'm sure this is news for most readers, still)...

but now I see that car uses the "empirical logit" function,
where plogis() provides the mathematical logit():

Not quite the empirical logit, because we don't know the counts, but a similar idea when the proportions include 0 or 1. Also, logit() recognizes percents as well as proportions, and so there's no need to convert the former to the latter.


The former is typically needed for data transformations where
you don't want to map {0,1} to  -/+ Inf but rather to finite
values ..

So I should stayed quiet, probably..

Well, I wouldn't go so far as that.

Best,
 John


Martin


     >> You can get a graph close to the one you shared via the following:

     >> library(car) # repeated so you don't omit it

     > .. and here you need 'car'  for the nice  probabilityAxis(.) ..

     >>> logits <- logit(y_values)
     >>> plot(x_value, logits, log="x", axes=FALSE,
     >> +      xlim=c(1, 200), ylim=logit(c(10, 99.999)),
     >> +      xlab="Precipitation Intensity (mm/d)",
     >> +      ylab="Cumulative Probability",
     >> +      main="Daily U.S. Precipitation",
     >> +      col="magenta")
     >>> axis(1, at=c(1, 2, 5, 10, 20, 50, 100, 200))
     >>> probabilityAxis(side=2, at=c(10, 30, 50, 90, 99, 99.9, 99.99,
     >> 99.999)/100)
     >>> box()

     >> Comments:

     >> This produces probabilities, not percents, on the vertical axis, which
     >> conforms to what the axis label says. Also, the ticks in the R version
     >> point out rather than into the plotting region -- the former is
     >> generally considered better practice. Finally, the graph is not a
     >> histogram as the original title states.

     >> I hope this helps,
     >> John


     >> --------------------------------------------
     >> John Fox
     >> Professor Emeritus
     >> McMaster University
     >> Hamilton, Ontario, Canada
     >> web: https://socialsciences.mcmaster.ca/jfox/

     >> On 7/27/2020 11:56 AM, Dileepkumar R wrote:
     >>> I think the attached sample figure is not visible
     >>> Here is the sample figure:
     >>> 
https://drive.google.com/file/d/16Uy3JD0wsEucUv_KOhXCxLZ4U-3wiBTs/view?usp=sharing
     >>>
     >>> sincerely,
     >>>
     >>>
     >>> Dileepkumar R
     >>>
     >>>
     >>>
     >>>
     >>> On Mon, Jul 27, 2020 at 7:13 PM Dileepkumar R <dileepkunj...@gmail.com>
     >>> wrote:
     >>>
     >>>> Dear All,
     >>>>
     >>>> I want to plot a simple cumulative probability distribution graph with
     >>>> like the attached screenshot.
     >>>> But I couldn't fix the y-axis scale as in that screenshot.
     >>>>
     >>>> My data details are follows:
     >>>>
     >>>> y_values
     >>>> 
=c(66.78149,76.10846,81.65518,85.06448,87.61703,89.61314,91.20297,92.36884,
     >>>> 
93.64070,94.57693,95.23052,95.75163,96.15792,96.58188,96.97933,97.29730,
     >>>> 
97.59760,97.91556,98.14520,98.37485,98.57799,98.74580,98.87829,99.06377,
     >>>> 
99.16093,99.25808,99.37290,99.45239,99.54072,99.59371,99.62904,99.66437,
     >>>> 
99.69970,99.70853,99.72620,99.73503,99.77036,99.79686,99.80569,99.82335,
     >>>> 
99.83219,99.84985,99.86751,99.87635,99.87635,99.90284,99.90284,99.90284,
     >>>> 
99.91168,99.92051,99.92051,99.93817,99.93817,99.93817,99.95584,99.95584,
     >>>> 99.97350,99.97350,99.97350,99.97350,99.97350,99.97350,99.97350)
     >>>>
     >>>> x_value=seq(63)
     >>>>
     >>>> Thank you all in advance
     >>>>
     >>>> Dileepkumar R
     >>>>
     >>>
     >>> [[alternative HTML version deleted]]
     >>>
     >>> ______________________________________________
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     >>> and provide commented, minimal, self-contained, reproducible code.
     >>>

     >> ______________________________________________
     >> R-help@r-project.org 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
     >> and provide commented, minimal, self-contained, reproducible code.

     > ______________________________________________
     > R-help@r-project.org 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
     > and provide commented, minimal, self-contained, reproducible code.

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