Not that I want to get deeply involved with this, but techniques that allow you
to take the random variation of the plots into account are badly needed in
practice. I mean, running qqnorm(rnorm(10)) a dozen times _ought_ to cure you
of overinterpreting plots that are not bang-on a straight line but have
apparent curvature, outliers, etc., but tell that to students these days...
Confidence bands would be good to include. Various techniques are in
circulation; I believe John Fox implemented one for Rcmdr/car. I seem to recall
that these are based on the binomial distribution or its asymptotic
approximation.
If you are qqnorm()-in residuals, the theory for constructing CI's for the
normal plot breaks down and you need to resort to simulation techniques, e.g.
something like
> Y <- rgamma(10, 5)
> qqnorm(scale(Y))
> for (i in 1:10) lines(qqnorm(sort(scale(rnorm(10))),plot=FALSE),
> col="lightgray")
-pd
On 04 Aug 2014, at 18:55 , Spencer Graves
<spencer.gra...@structuremonitoring.com> wrote:
> Hi, Jim et al.:
>
>
> Thanks for the reply, Jim.
>
>
> What are your favorite examples using normal probability plots to
> identify outliers, skewness, kurtosis, mixtures, and the need for
> transformations in plots of raw data and residuals from model fits -- or
> using half-normal plots with estimated parameters?
>
>
> I ask, because I believe the current Wikipedia article on "Normal
> probability plot" could be improved dramatically with a set of great
> examples. I also plan to write a function to display a normal probability
> plot with a histogram, a density estimate, and a boxplot and use it with the
> examples. My current plan is to create this with a function qqnormPlus(...,
> datax=TRUE, histargs, densityargs, boxplotargs) that passes histargs,
> densityargs and boxplotargs to truehist{MASS}, density, and boxplot,
> respectively. I plan to add this to the Ecfun package (for which I'm the
> author and maintainer).
>
>
> This wikipedia article received 14774 views in the past 90 days,
> averaging not quite 700 per day, so I think it's worth doing. I could use
> suggestions (and help from other Wikipedians on this list).
>
>
> Thanks,
> Spencer
>
>
> On 8/3/2014 2:59 PM, Jim Lemon wrote:
>> Hi Spencer,
>> The last example for the twoord.plot function (plotrix) does this.
>>
>> Jim
>>
>> On Sun, 3 Aug 2014 12:38:10 PM Spencer Graves wrote:
>>> Does a function exist that combines a normal probability plot
>>> with a histogram and maybe a density estimate on the same plot?
>>>
>>>
>>> I'm revising the Wikipedia article on "Normal probability plot",
>>> and I think it would be good to provide examples of this.
>>>
>>>
>>> Thanks,
>>> Spencer
>>>
>>>
>>> p.s. Please reply also with suggestions for how to improve that
>>> Wikipedia article if you feel so inclined.
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list
>>> 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
> 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.
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
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