On 18/10/14 12:41, Bill Oliver wrote:
On Fri, 17 Oct 2014, Ranjan Maitra wrote:
Hi,
On Fri, 17 Oct 2014 14:30:08 +0000 Bill Oliver <vendor@xxxxxxxxxxxxx>
wrote:
3) One specific statistics package that I use about four times a year
Just curious: what is this specific statistics package that you use
about four times a year? R can not substitute for this?
R is good, and I love it. However, fitting distributions is a pain,
because you basically have to do it by hand. So, I use EasyFit:
http://www.mathwave.com/easyfit-distribution-fitting.html
This makes it a breeze. I have not been able to find an R package that
does this. All the R packages I've seen make you build your own library
of probability density functions and then do the fitting on each one.
If you know of a package in R that replicates what easyfit does, I'd
**love** to use it.
Happily, I use it seldom enough that I can keep re-installing and using
the demo.
It would be a bit of work, although not an overwhelming intellectual
challenge, to produce an R package that would do essentially the same
thing as "easyfit". There are a number of questions that would have to
be addressed of course. E.g. just how do you want/expect the
distributions to be fitted to the data? Maximum likelihood? Are all
the distributions dealt with by "easyfit" amenable to being fitted via
maximum likelihood? And how is the choice of distribution to be made?
AIC? The "easyfit" web page refers to "goodness of fit tests", which
can be problematic, or "visual inspection" --- always a good idea, but
it too can be problematic.
Overall I don't think this "press a button and let the software do your
thinking for you" is the right way to go. If the results matter at all,
you need to know what you are doing and what pitfalls can lurk to trap
the unwary.
I don't understand what you mean by "All the R packages I've seen make
you build your own library of probability density functions and then do
the fitting on each one." R has a large number of built-in probability
density functions (including *most* of the distributions listed on the
"easyfit" web page) and most of these can be fitted (via maximum
likelihood) using the fitdistr() function from the MASS package. The
fitdistr() function can fit essentially any distribution for which a
probability density function can be written. Goodness of fit testing is
more problematic, but then as I said that is a problematic topic.
Superimposing fitted pdf-s on a histogram of the data for "visual
comparison" is straightforward.
cheers,
Rolf Turner
--
Rolf Turner
Technical Editor ANZJS
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