[Rd] pbinom with size argument 0 (PR#8560)

[P Ehlers]

(Ted Harding) wrote:
> On 03-Feb-06 Peter Dalgaard wrote:
> 
>>(Ted Harding) <Ted.Harding at nessie.mcc.ac.uk> writes:
>>
>>
>>>On 03-Feb-06 uht at dfu.min.dk wrote:
>>>
>>>>Full_Name: Uffe Høgsbro Thygesen
>>>>Version: 2.2.0
>>>>OS: linux
>>>>Submission from: (NULL) (130.226.135.250)
>>>>
>>>>
>>>>Hello all.
>>>>
>>>>  pbinom(q=0,size=0,prob=0.5)
>>>>
>>>>returns the value NaN. I had expected the result 1. In fact any
>>>>value for q seems to give an NaN.
>>>
>>>Well, "NaN" can make sense since "q=0" refers to a single sampled
>>>value, and there is no value which you can sample from "size=0";
>>>i.e. sampling from "size=0" is a non-event. I think the probability
>>>of a non-event should be NaN, not 1! (But maybe others might argue
>>>that if you try to sample from an empty urn you necessarily get
>>>zero "successes", so p should be 1; but I would counter that you
>>>also necessarily get zero "failures" so q should be 1. I suppose
>>>it may be a matter of whether you regard the "r" of the binomial
>>>distribution as referring to the "identities" of the outcomes
>>>rather than to how many you get of a particular type. Hmmm.)
>>>
>>>
>>>>Note that
>>>>
>>>>  dbinom(x=0,size=0,prob=0.5)
>>>>
>>>>returns the value 1.
>>>
>>>That is probably because the .Internal code for pbinom may do
>>>a preliminary test for "x >= size". This also makes sense, for
>>>the cumulative p<dist> for any <dist> with a finite range,
>>>since the answer must then be 1 and a lot of computation would
>>>be saved (likewise returning 0 when x < 0). However, it would
>>>make even more sense to have a preceding test for "size<=0"
>>>and return NaN in that case since, for the same reasons as
>>>above, the result is the probability of a non-event.
>>
>>Once you get your coffee, you'll likely realize that you got
>>your p's and d's mixed up...
> 
> 
> You're right about the mix-up! (I must mend the pipeline.)
> 
> 
>>I think Uffe is perfectly right: The result of zero experiments will
>>be zero successes (and zero failures) with probability 1, so the
>>cumulative distribution function is a step function with one step at
>>zero ( == as.numeric(x>=0) ).
> 
> 
> I'm perfectly happy with this argument so long as it leads to
> dbinom(x=0,size=0,prob=p)=1 and also pbinom(q=0,size=0,prob=p)=1
> (which seems to be what you are arguing too). And I think there
> are no traps if p=0 or p=1.
> 
> 
>>>(But it depends on your point of view, as above ... However,
>>>surely the two  should be consistent with each other.)
> 
> 
> Ted.

I prefer a (consistent) NaN. What happens to our notion of a
Binomial RV as a sequence of Bernoulli RVs if we permit n=0?
I have never seen (nor contemplated, I confess) the definition
of a Bernoulli RV as anything other than some dichotomous-outcome
one-trial random experiment. Not n trials, where n might equal zero,
but _one_ trial. I can't see what would be gained by permitting a
zero-trial experiment. If we assign probability 1 to each outcome,
we have a problem with the sum of the probabilities.

Peter Ehlers
> 
> --------------------------------------------------------------------
> E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>
> Fax-to-email: +44 (0)870 094 0861
> Date: 03-Feb-06                                       Time: 15:07:49
> ------------------------------ XFMail ------------------------------
> 
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel