[Rd] plogis (and other p* functions), vectorized lower.tail
@oko| @end|ng |rom |n@@-tou|ou@e@|r
Thu Dec 9 17:13:36 CET 2021
On 09/12/2021 16:55, Ben Bolker wrote:
> On 12/9/21 10:03 AM, Martin Maechler wrote:
>>>>>>> Matthias Gondan
>>>>>>> on Wed, 8 Dec 2021 19:37:09 +0100 writes:
>> > Dear R developers,
>> > I have seen that plogis silently ignores vector elements of
>> and also of 'log'.
>> This is indeed the case for all d*, p*, q* functions.
>> Yes, this has been on purpose and therefore documented, in the
>> case of plogis, e.g. in the 'Value' section of ?plogis :
>> The length of the result is determined by ‘n’ for ‘rlogis’, and is
>> the maximum of the lengths of the numerical arguments for the
>> other functions.
>> (note: *numerical* arguments: the logical ones are not recycled)
>> The numerical arguments other than ‘n’ are recycled to the length
>> of the result. Only the first elements of the logical arguments
>> are used.
>> (above, we even explicitly mention the logical arguments ..)
>> Recycling happens for the first argument (x,p,q) of these
>> functions and for "parameters" of the distribution, but not for
>> lower.tail, log.p (or 'log').
>> >> plogis(q=0.5, location=1, lower.tail=TRUE)
>> >  0.3775407
>> >> plogis(q=0.5, location=1, lower.tail=FALSE)
>> >  0.6224593
>> >> plogis(q=c(0.5, 0.5), location=1, lower.tail=c(TRUE, FALSE))
>> >  0.3775407 0.3775407
>> > For those familiar with psychological measurement: A use case
>> of the above function is the so-called Rasch model, where the
>> probability that a person with some specific ability (q) makes a
>> correct (lower.tail=TRUE) or wrong response (lower.tail=FALSE) to an
>> item with a specific difficulty (location). A vectorized version of
>> plogis would enable to determine the likelihood of an entire response
>> vector in a single call. My current workaround is an intermediate
>> call to „Vectorize“.
>> > I am wondering if the logical argument of lower.tail can be
>> vectorized (?). I see that this may be a substantial change in many
>> places (basically, all p and q functions of probability
>> distributions), but in my understanding, it would not break existing
>> code which assumes lower.tail to be a single element. If that’s not
>> > possible/feasible, I suggest to issue a warning if a vector of
>> length > 1 is given in lower.tail. I am aware that the documentation
>> clearly states that lower.tail is a single boolean.
>> aah ok, here you say you know that the current behavior is documented.
>> > Thank you for your consideration.
>> As you mention, changing this would be quite a large endeavor.
>> I had thought about doing that many years ago, not remembering
>> details, but seeing that in almost all situations you really
>> only need one of the two tails (for Gaussian- or t- based confidence
>> intervals you also only need one, for symmetry reason).
>> Allowing the recycling there would make the intermediate C code
>> (which does the recycling) larger and probably slightly
>> slower because of conceptually two more for loops which would in
>> 99.9% only have one case ..
>> I'd have found that ugly to add. ... ...
>> ... but of course, if you can prove that the code bloat would not be
>> and not deteriorate speed in a measurable way and if you'd find
>> someone to produce a comprehensive and tested patch ...
>> > With best wishes,
>> > Matthias
>> > [[alternative HTML version deleted]]
>> > ______________________________________________
>> > R-devel using r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-devel
>> R-devel using r-project.org mailing list
> I agree with everything said above, but think that adding a warning
> when length(lower.tail) > 1 (rather than silently ignoring) might be
> helpful ... ??
> As for the vectorization, it seems almost trivial to do at the user
> level when needed (albeit it's probably a little bit inefficient):
> pv <- Vectorize(plogis, c("q", "location", "scale", "lower.tail"))
> pv(q=c(0.5, 0.5), location=1, lower.tail=c(TRUE, FALSE))
>  0.3775407 0.6224593
.. or directly use mapply()
mapply(plogis, q=c(0.5, 0.5), location=1, lower.tail=c(TRUE, FALSE))
 0.3775407 0.6224593
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