Re: st: CDF plot with normal probability axis

[Nick Cox <njcoxstata@gmail.com>]

-distplot- (SJ), -cdfplot- (STB originally, SSC now): as always,
please explain the origin of the user-written commands you refer to.

-qplot- (SJ) can do this, roughly.

. sysuse auto
(1978 Automobile Data)

. qplot turn trunk, trscale(invnormal(@))

. qplot turn trunk, trscale(invnormal(@)) xtitle(standard normal
deviate) xla(-2/2)

The axes are the other way round from what you ask; I'd argue that is
better practice, or at least consistent with -qnorm-. (-ysc(log)- is
also possible.)

Note that you should not expect cumulative distribution plots to do
this by default as they usually plot cumulative probabilities as 1/n,
..., n/n and -invormal(n/n)- is -invnormal(1)- and as such
indeteminate.

But it is as easy to do this pretty much from first principles. See e.g.

http://www.stata.com/support/faqs/statistics/percentile-ranks-and-plotting-positions/index.html

http://www.stata-journal.com/sjpdf.html?articlenum=gr0027

http://www.stata-journal.com/sjpdf.html?articlenum=gr0032

I will cheat slightly and use -mylabels- (SSC).

Here is some code. Any number of possible small variations should be evident.

sysuse auto, clear

replace price = price/1000

foreach v in price mpg {
    egen y`v' = rank(`v')
    su `v', meanonly
    replace y`v' = invnormal((y`v' - 0.5) / r(N))
    label var y`v' "`: var label `v''"
}

mylabels 1 5 10(10)90 95 99, myscale(invnormal(@/100)) local(labels)

twoway connect yprice price, ms(Dh) sort || ///
connect ympg mpg, sort ms(Th) xsc(log) yla(`labels', ang(h)) xla(5 10 20 40) ///
ytitle(Cumulative percent)

Nick
njcoxstata@gmail.com


On 14 November 2013 02:09, Livingston, Michael (TP)
<MichaelL@turningpoint.org.au> wrote:

> I'm trying to create  simple plot to compare two distributions - I want the logged values across the x-axis and then the cumulative probability on the y-axis, but with a normal probability y-axis (like the one here: http://lowrank.net/gnuplot/plot7-e.html).
>
> It seems like it should be really simple, but I haven't come with any solutions using distplot or cdfplot. Is there something obvious I'm missing?

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