on 17/11/2003 18:07, Nick Cox at [email protected] wrote:
> Ernest Berkhout has already answered
> "histogram", and that's probably the best
> short answer.
One of the things that Stata won�t do for you, which is a terrible lesson to
anyone who tries it, is allow you to see what happens as you change
* the bin width and
* the start value
for a histogram. I give a pretty disconcerting demonstration with JMP (which
I use for EDA) in which I do each of these things in turn, with the
histogram redrawing in real time. The result is that you can make a
histogram change shape utterly and in a totally arbitrary way.
This is a real problem. We assume that features of graphical displays are
data features, and the differences between displays are differences in the
data displayed. This simply isn't so with the histogram.
Kernel smoothing is a better solution, but again you need to be able to
'tune' the smoother to view coarse and fine detail.
Boxplots at least show you definable features of your data. Various
extensions of these which also show data density have been proposed: violin
plots (Stata version by Thomas J. Steichen) are promising, but have been
criticised for the potentially misleading results of kernal smoothing. An
alternative, box percentile plots, has been proposed by Esty and Banfeld
(download it from here:
http://www.jstatsoft.org/v08/i17/BoxPercentilePlot.pdf)
which looks good to me, but I am not aware of anyone taking it up.
Finally, Nick Cox has done extensive work on univariate displays, notably on
-catplot- and -distplot-. The whole topic of how you display univariate data
depends heavily on *why* you want to display it.
But for sure, no matter what you want to do, the histogram is inferior to at
least one of the alternatives. It's kinda like that tank division: it
appears to offer a solution to all the problems of the world. Don't be
fooled.
Ronan M Conroy ([email protected])
Lecturer in Biostatistics
Royal College of Surgeons
Dublin 2, Ireland
+353 1 402 2431 (fax 2764)
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