[SciPy-dev] scipy.stats: sf for symmetric distributions.

josef.pktd@gmai... josef.pktd@gmai...
Wed Sep 16 16:27:14 CDT 2009


On Wed, Sep 16, 2009 at 5:13 PM,  <josef.pktd@gmail.com> wrote:
> On Wed, Sep 16, 2009 at 3:53 PM, David Warde-Farley <dwf@cs.toronto.edu> wrote:
>> On 16-Sep-09, at 3:17 PM, josef.pktd@gmail.com wrote:
>>
>>> Did you check for which distributions this would apply?
>>> I remember there was some problem that pymvpa had with the r
>>> distribution because the numerical integration close to the lower
>>> bound didn't work.
>>
>> I haven't gone through the list exhaustively but I'm fairly certain it
>> will make a positive difference for the t-distribution and the
>> standard normal, especially since these are used in statistical tests
>> where people care about their p-values.
>
> good, your changes are similar to what Per proposed in
> http://projects.scipy.org/scipy/changeset/5602/trunk/scipy/stats/distributions.py
>
> There might be a few more cases like these.
>
>
>>
>> I just committed a fix for these two distributions, we can weigh the
>> worth of subclassing or not further (incidentally, this breaks two
>> ttest tests because they differ in the 17th decimal place, how sure
>> are you of those hard-coded numbers?).
>>
>
> I got busy and just saw your follow up.
> If there is a request for higher than standard precision of
> floating point calculationss, then it should be a bug in the
> test. I guess, I missed adding decimal if it is in one of
> my tests.
>
> Which ttest test is it?

I saw the changes, Hooray we don't reject the null with
0.42264973081037421 instead of 0.42264973081037427 probability ;)

I think these tests should have used assert_almost_equal because of
the float. I might have been distracted because of the integer. And
these are some of my first tests in scipy, which might still be a bit
shaky.
These tests are just regression tests (as it says on top), so any
improvement in numerical precision might require some
adjustments.

Josef

>
> Josef
>
>
>> David
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>


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