On 06/02/2011 01:15 PM, Roger Lew wrote:
Hi Josef,
Thanks for the feedback. The "choice" of using cdf is more a carryover from how the algorithm is described by Gleason. Perhaps it would be best to have it match your intuition and accept the survival function?
Feel free to treat it like your own for statsmodels. I will definitely check out some of your multcomp module so I'm not reinventing the wheel.
In the grand scheme, I could see these having a home in scipy.special (after more extensive review of course). That is where I went to look for it when I didn't find it in distributions.
Roger
On Thu, Jun 2, 2011 at 1:38 AM, <josef.pktd@gmail.com <mailto:josef.pktd@gmail.com>> wrote:
On Thu, Jun 2, 2011 at 12:53 AM, Roger Lew <rogerlew@gmail.com <mailto:rogerlew@gmail.com>> wrote: > Hi, > I have implemented some approximations for studentized range quantiles and > probabilities based on John R. Gleason's (1999) "An accurate, non-iterative > approximation for studentized range quantiles." Computational Statistics & > Data Analysis, (31), 147-158. > Probability approximations rely on scipy.optimize.fminbound. The functions > accept both scalars or array-like data thanks to numpy.vectorize. A fair > amount of validation and testing has been conducted on the code. More > details can be found here: http://code.google.com/p/qsturng-py/ > I welcome any thoughts as to whether you all think this might be useful to > add to SciPy or make into a scikit. Any general comments would be helpful as > well. I should mention I'm a cognitive neuroscientist by trade, my use of > statistical jargon probably isn't that good.
Hi Roger,
I'm very interested in using this in scikits.statsmodels. The table that I am currently using is very limited http://statsmodels.sourceforge.net/devel/generated/scikits.statsmodels.sandb...
>From a quick look it looks very good. What I found a bit confusing is that qstrung takes the probability of the cdf and not of the survival function. Without reading the docstring carefully enough, I interpreted it as a p-value (upper tail) especially since pstrung returns the upper tail probability,
>>> import scikits.statsmodels.sandbox.stats.multicomp as smmc >>> for i in range(3, 10): x = qsturng(0.95, i, 16) x, psturng(x, i, 16), smmc.get_tukeyQcrit(i, 16, 0.05), smmc.tukey_pvalues(x*np.ones(i), 16)[0]
(3.647864471854692, 0.049999670839029453, array(3.6499999999999999), 0.050092818925981608) (4.0464124382823847, 0.050001178443752514, array(4.0499999999999998), 0.037164602483501508) (4.3332505094058114, 0.049999838126148499, array(4.3300000000000001), 0.029954033157223781) (4.5573603020371234, 0.049999276281813887, array(4.5599999999999996), 0.025276987281047769) (4.7410585998112742, 0.049998508166777755, array(4.7400000000000002), 0.022010630154416622) (4.8965400268915289, 0.04999983345598491, array(4.9000000000000004), 0.019614841752159107) (5.0312039650945257, 0.049999535359310343, array(5.0300000000000002), 0.017721848279719898)
The last column is (in my interpretation) supposed to be 0.05. I was trying to get the pvalues for Tukeys range statistic through the multivariate t-distribution, but the unit test looks only at one point (and I ran out of time to work on this during Christmas break). Either there is a bug (it's still in the sandbox) or my interpretation is wrong.
The advantage of the multivariate t-distribution is that it allows for arbitrary correlation, but it's not a substitute for pre-calculated tables for standard cases/distributions because it's much too slow.
------------ As a bit of background on the multiple testing, multiple comparison status in statsmodels:
The tukeyhsd test has one test case against R, but it has too many options (it allows unequal variances and unequal sample sizes, that still need to be checked.)
http://statsmodels.sourceforge.net/devel/generated/scikits.statsmodels.sandb...
What I did manage to finish and verify against R
http://statsmodels.sourceforge.net/devel/generated/scikits.statsmodels.sandb...
multiple testing for general linear models is very incomplete
and as an aside: I'm not a statistician, and if the module in the statsmodels sandbox is still a mess then it's because I took me a long time and many functions to figure out what's going on. ----------
scipy.special has a nice collection of standard distributions functions, but it would be very useful to have some additional distributions either in scipy or scikits.statsmodels available, like your studentized range statistic, (and maybe some others in multiple comparisons, like Duncan, Dunnet) and Anderson-Darling, and ...
Thanks,
Josef
> Regards, > Roger > Roger Lew > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev@scipy.org <mailto:SciPy-Dev@scipy.org> > http://mail.scipy.org/mailman/listinfo/scipy-dev > > _______________________________________________ SciPy-Dev mailing list SciPy-Dev@scipy.org <mailto:SciPy-Dev@scipy.org> http://mail.scipy.org/mailman/listinfo/scipy-dev
_______________________________________________ SciPy-Dev mailing list SciPy-Dev@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-dev The problem I have is that this is still an approximation that probably covers what most situations. Do you have the Copenhaver & Holland (1988) approach or know any BSD-licensed Python code for it?
Also, the code probably needs to conform to numpy/scipy standards (which I don't remember the links). You also have vary less desirable features: 1) hard coded numbers like '1e38' - numpy does define infinity (np.inf). 2) comparisons to 'inf' ('v==inf') that are not desirable. Bruce