[SciPy-User] stats.pearsonr divide by zero warning

[josef.pktd at gmail.com]

On Mon, Aug 9, 2010 at 3:46 PM,  <josef.pktd at gmail.com> wrote:
> On Mon, Aug 9, 2010 at 3:19 PM, Zachary Pincus <zachary.pincus at yale.edu> wrote:
>> Hello,
>>
>> I just svn-up'd scipy, and now find that stats.pearsonr is causing
>> divide-by-zero warnings foolishly.
>>
>> the function contains the following stanzas:
>>
>>     rs = np.corrcoef(ar,br,rowvar=axisout)
>>
>>     t = rs * np.sqrt((n-2) / ((rs+1.0)*(1.0-rs)))
>>     prob = distributions.t.sf(np.abs(t),n-2)*2
>>
>>     if rs.shape == (2,2):
>>         return rs[1,0], prob[1,0]
>>     else:
>>         return rs, prob
>>
>> Given that the diagonal of the correlation matrix returned by corrcoef
>> will *always* be 1s, the t matrix will have divide-by-zero issues on
>> the diagonal, and give inf values -- which get zero values for the t-
>> distribution's survival function, so everything's fine, output-wise.
>> Presumably, though, the t-calculating line should be flanked by err =
>> np.seterr(divide='ignore') / np.seterr(**err), right?
>>
>> Should I add a bug in the tracker? Someone want to just commit this fix?
>
> I guess you mean spearmanr,  pearsonr hasn't been rewritten as far as I can see.
>
> The old trick (still used in pearsonr) was to add TINY in the
> calculation of the test statistic.
> Maybe we should add TINY to the diagonal, which would keep a zero
> division warning if any of the series are perfectly correlated.
>
> seterr is also fine with me.
>
> a ticket is always good, at least for the record so we know what to
> watch out for. I have warnings turned off globally, so no zero
> division problems for me.
>
> np.corrcoef might throw a warning if there is zero variance, but I'm
> not sure this applies in this case
>
> Josef

just a follow-up because I think there is a similar case in the
contingency table code

mut_inf = np.nansum(self.probability * np.log(self.observed / self.expected))

Do we really need to protect everywhere for zero division warnings?

I think in statsmodels we worked around the warning in 0*np.log(0) or
something like this.

Josef

>
>
>
>>
>> Zach
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>