[SciPy-User] R vs Python for simple interactive data analysis

josef.pktd@gmai... josef.pktd@gmai...
Mon Aug 29 12:13:48 CDT 2011

On Mon, Aug 29, 2011 at 12:59 PM,  <josef.pktd@gmail.com> wrote:
> On Mon, Aug 29, 2011 at 11:42 AM,  <josef.pktd@gmail.com> wrote:
>> On Mon, Aug 29, 2011 at 11:34 AM, Christopher Jordan-Squire
>> <cjordan1@uw.edu> wrote:
>>> On Mon, Aug 29, 2011 at 10:27 AM,  <josef.pktd@gmail.com> wrote:
>>>> On Mon, Aug 29, 2011 at 11:10 AM, Skipper Seabold <jsseabold@gmail.com> wrote:
>>>>> On Mon, Aug 29, 2011 at 10:57 AM, Christopher Jordan-Squire
>>>>> <cjordan1@uw.edu> wrote:
>>>>>> On Sun, Aug 28, 2011 at 2:54 PM, Skipper Seabold <jsseabold@gmail.com> wrote:
>>>>>>> On Sat, Aug 27, 2011 at 10:15 PM, Bruce Southey <bsouthey@gmail.com> wrote:
>>>>>>>> On Sat, Aug 27, 2011 at 5:06 PM, Wes McKinney <wesmckinn@gmail.com> wrote:
>>>>>>>>> On Sat, Aug 27, 2011 at 5:03 PM, Jason Grout
>>>>>>>>> <jason-sage@creativetrax.com> wrote:
>>>>>>>>>> On 8/27/11 1:19 PM, Christopher Jordan-Squire wrote:
>>>>>>>>>>> This comparison might be useful to some people, so I stuck it up on a
>>>>>>>>>>> github repo. My overall impression is that R is much stronger for
>>>>>>>>>>> interactive data analysis. Click on the link for more details why,
>>>>>>>>>>> which are summarized in the README file.
>>>>>>>>>>  From the README:
>>>>>>>>>> "In fact, using Python without the IPython qtconsole is practically
>>>>>>>>>> impossible for this sort of cut and paste, interactive analysis.
>>>>>>>>>> The shell IPython doesn't allow it because it automatically adds
>>>>>>>>>> whitespace on multiline bits of code, breaking pre-formatted code's
>>>>>>>>>> alignment. Cutting and pasting works for the standard python shell,
>>>>>>>>>> but then you lose all the advantages of IPython."
>>>>>>>>>> You might use %cpaste in the ipython normal shell to paste without it
>>>>>>>>>> automatically inserting spaces:
>>>>>>>>>> In [5]: %cpaste
>>>>>>>>>> Pasting code; enter '--' alone on the line to stop.
>>>>>>>>>> :if 1>0:
>>>>>>>>>> :    print 'hi'
>>>>>>>>>> :--
>>>>>>>>>> hi
>>>>>>>>>> Thanks,
>>>>>>>>>> Jason
>>>>>>>>>> _______________________________________________
>>>>>>>>>> SciPy-User mailing list
>>>>>>>>>> SciPy-User@scipy.org
>>>>>>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>>>>>> This strikes me as a textbook example of why we need an integrated
>>>>>>>>> formula framework in statsmodels. I'll make a pass through when I get
>>>>>>>>> a chance and see if there are some places where pandas would really
>>>>>>>>> help out.
>>>>>>>> We used to have a formula class is scipy.stats and I do not follow
>>>>>>>> nipy (http://nipy.sourceforge.net/nipy/stable/index.html) as it also
>>>>>>>> had this (extremely flexible but very hard to comprehend). It was what
>>>>>>>> I had argued was needed ages ago for statsmodel. But it needs a
>>>>>>>> community effort because the syntax required serves multiple
>>>>>>>> communities with different annotations and needs. That is also seen
>>>>>>>> from the different approaches taken by the stats packages from S/R,
>>>>>>>> SAS, Genstat (and those are just are ones I have used).
>>>>>>> We have held this discussion at _great_ length multiple times on the
>>>>>>> statsmodels list and are in the process of trying to integrate
>>>>>>> Charlton (from Nathaniel) and/or Formula (from Jonathan / NiPy) into
>>>>>>> the statsmodels base.
>>>>>>> http://statsmodels.sourceforge.net/dev/roadmap_todo.html#formula-framework
>>>>>>> and more recently
>>>>>>> https://groups.google.com/group/pystatsmodels/browse_thread/thread/a76ea5de9e96964b/fd85b80ae46c4931?
>>>>>>> https://github.com/statsmodels/formula
>>>>>>> https://github.com/statsmodels/charlton
>>>>>>> Wes and I made some effort to go through this at SciPy. From where I
>>>>>>> sit, I think it's difficult to disentangle the data structures from
>>>>>>> the formula implementation, or maybe I'd just prefer to finish
>>>>>>> tackling the former because it's much more straightforward. So I'd
>>>>>>> like to first finish the pandas-integration branch that we've started
>>>>>>> and then focus on the formula support. This is on my (our, I hope...)
>>>>>>> immediate long-term goal list. Then I'd like to come back to the
>>>>>>> community and hash out the 'rules of the game' details for formulas
>>>>>>> after we have some code for people to play with, which promises to be
>>>>>>> "fun."
>>>>>>> https://github.com/statsmodels/statsmodels/tree/pandas-integration
>>>>>>> FWIW, I could also improve the categorical function to be much nicer
>>>>>>> for the given examples (ie., take a list, drop a reference category),
>>>>>>> but I don't know that it's worth it, because it's really just a
>>>>>>> stop-gap and ideally users shouldn't have to rely on it. Thoughts on
>>>>>>> more stop-gap?
>>>>>> I want more usability, but I agree that a stop-gap probably isn't the
>>>>>> right way to go, unless it has things we'd eventually want anyways.
>>>>>>> If I understand Chris' concerns, I think pandas + formula will go a
>>>>>>> long way towards bridging the gap between Python and R usability, but
>>>>>> Yes, I agree. pandas + formulas would go a long, long way towards more
>>>>>> usability.
>>>>>> Though I really, really want a scatterplot smoother (i.e., lowess) in
>>>>>> statsmodels. I use it a lot, and the final part of my R file was
>>>>>> entirely lowess. (And, I should add, that was the part people liked
>>>>>> best since one of the main goals of the assignment was to generate
>>>>>> nifty pictures that could be used to summarize the data.)
>>>>> Working my way through the pull requests. Very time poor...
>>>>>>> it's a large effort and there are only a handful (at best) of people
>>>>>>> writing code -- Wes being the only one who's more or less "full time"
>>>>>>> as far as I can tell. The 0.4 statsmodels release should be very
>>>>>>> exciting though, I hope. I'm looking forward to it, at least. Then
>>>>>>> there's only the small problem of building an infrastructure and
>>>>>>> community like CRAN so we can have specialists writing and maintaining
>>>>>>> code...but I hope once all the tools are in place this will seem much
>>>>>>> less daunting. There certainly seems to be the right sentiment for it.
>>>>>> At the very least creating and testing models would be much simpler.
>>>>>> For weeks I've been wanting to see if gmm is the same as gee by
>>>>>> fitting both models to the same dataset, but I've been putting it off
>>>>>> because I didn't want to construct the design matrices by hand for
>>>>>> such a simple question. (GMM--Generalized Method of Moments--is a
>>>>>> standard econometrics model and GEE--Generalized Estimating
>>>>>> Equations--is a standard biostatics model. They're both
>>>>>> generalizations of quasi-likelihood and appear very similar, but I
>>>>>> want to fit some models to figure out if they're exactly the same.)
>>>> Since GMM is still in the sandbox, the interface is not very polished,
>>>> and it's missing some enhancements. I recommend asking on the mailing
>>>> list if it's not clear.
>>>> Note GMM itself is very general and will never be a quick interactive
>>>> method. The main work will always be to define the moment conditions
>>>> (a bit similar to non-linear function estimation, optimize.leastsq).
>>>> There are and will be special subclasses, eg. IV2SLS, that have
>>>> predefined moment conditions, but, still, it's up to the user do
>>>> construct design and instrument arrays.
>>>> And as far as I remember, the GMM/GEE package in R doesn't have a
>>>> formula interface either.
>>> Both of the two gee packages in R I know of have formula interfaces.
>>> http://cran.r-project.org/web/packages/geepack/
>>> http://cran.r-project.org/web/packages/gee/index.html
> This is very different from what's in GMM in statsmodels so far. The
> help file is very short, so I'm mostly guessing.
> It seems to be for (a subset) of generalized linear models with
> longitudinal/panel covariance structures. Something like this will
> eventually (once we get panel data models)  as a special case of GMM
> in statsmodels, assuming it's similar to what I know from the
> econometrics literature.
> Most of the subclasses of GMM that I currently have, are focused on
> instrumental variable estimation, including non-linear regression.
> This should be expanded over time.
> But GMM itself is designed for subclassing by someone who wants to use
> her/his own moment conditions, as in
> http://cran.r-project.org/web/packages/gmm/index.html
> or for us to implement specific models with it.
> If someone wants to use it, then I have to quickly add the options for
> the kernels of the weighting matrix, which I keep postponing.
> Currently there is only a truncated, uniform kernel that assumes
> observations are order by time, but users can provide their own
> weighting function.
> Josef
>> I have to look at this. I mixed up some acronyms, I meant GEL and GMM
>> http://cran.r-project.org/web/packages/gmm/index.html
>> the vignette was one of my readings, and the STATA description for GMM.
>> I never really looked at GEE. (That's Skipper's private work so far.)
>> Josef
>>> -Chris JS
>>>> Josef
>>>>> Oh, it's not *that* bad. I agree, of course, that it could be better,
>>>>> but I've been using mainly Python for my work, including GMM and
>>>>> estimating equations models (mainly empirical likelihood and
>>>>> generalized maximum entropy) for the last ~two years.
>>>>> Skipper
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User@scipy.org
>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>> _______________________________________________
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just to make another point:

Without someone adding mixed effects, hierachical, panel/longitudinal
models, and .... it will not help to have a formula interface to them.
(Thanks to Scott we will soon have survival)


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