Thank you for a fast response. Nice to see this mailing list being so alive. Regarding Inf issue: I agree with your assessment that Inf should not be removed. The code gave me an impression that Inf values were intentionally removed (since is.finite() was used everywhere, except for paired case). I will try to adjust my patch according to your feedback. One more thing: it seems like you assumed that issues 2:4 are all related to machine precision, which is not the case - only 2nd issue is. Just wanted to draw this to your attention, in case you might have some feedback and guidelines about issues 3 and 4 as well. On 2019-12-07 21:59, Martin Maechler wrote:>>>>>> Karolis Koncevi?ius >>>>>> on Sat, 7 Dec 2019 20:55:36 +0200 writes: > > > Hello, > > Writing to share some things I've found about wilcox.test() that seem a > > a bit inconsistent. > > > 1. Inf values are not removed if paired=TRUE > > > # returns different results (Inf is removed): > > wilcox.test(c(1,2,3,4), c(0,9,8,7)) > > wilcox.test(c(1,2,3,4), c(0,9,8,Inf)) > > > # returns the same result (Inf is left as value with highest rank): > > wilcox.test(c(1,2,3,4), c(0,9,8,7), paired=TRUE) > > wilcox.test(c(1,2,3,4), c(0,9,8,Inf), paired=TRUE) > >Now which of the two cases do you consider correct ? > >IHMO, the 2nd one is correct: it is exactly one property of the >*robustness* of the wilcoxon test and very desirable that any >(positive) outlier is treated the same as just "the largest >value" and the test statistic (and hence the p-value) should not >change. > >So I think the first case is wrong, notably if modified, (such >that the last y is the overall maximal value (slightly larger sample): > >> wilcox.test(1:7, 1/8+ c(9:4, 12)) > > Wilcoxon rank sum test > >data: 1:7 and 1/8 + c(9:4, 12) >W = 6, p-value = 0.01748 >alternative hypothesis: true location shift is not equal to 0 > >> wilcox.test(1:7, 1/8+ c(9:4, 10000)) > > Wilcoxon rank sum test > >data: 1:7 and 1/8 + c(9:4, 10000) >W = 6, p-value = 0.01748 >alternative hypothesis: true location shift is not equal to 0 > >> wilcox.test(1:7, 1/8+ c(9:4, Inf)) > > Wilcoxon rank sum test > >data: 1:7 and 1/8 + c(9:4, Inf) >W = 6, p-value = 0.03497 >alternative hypothesis: true location shift is not equal to 0 > >The Inf case should definitely give the same as the 10'000 case. >That's exactly one property of a robust statistic. > >Thank you, Karolis, this is pretty embarrassing to only be detected now after 25+ years of R in use ... > >The correct fix starts with replacing the is.finite() by !is.na() and keep the 'Inf' in the rank computations... >(but then probably also deal with the case of more than one Inf, notably the Inf - Inf "exception" which is not triggered by your example...) > > >--- > >Ben addressed the "rounding" / numerical issues unavoidable for >the other problems. > > > 2. tolerance issues with paired=TRUE. > > > wilcox.test(c(4, 3, 2), c(3, 2, 1), paired=TRUE) > > # ... > > # Warning: cannot compute exact p-value with ties > > > wilcox.test(c(0.4,0.3,0.2), c(0.3,0.2,0.1), paired=TRUE) > > # ... > > # no warning > > > 3. Always 'x observations are missing' when paired=TRUE > > > wilcox.test(c(1,2), c(NA_integer_,NA_integer_), paired=TRUE) > > # ... > > # Error: not enough (finite) 'x' observations > > > 4. No indication if normal approximation was used: > > > # different numbers, but same "method" name > > wilcox.test(rnorm(10), exact=FALSE, correct=FALSE) > > wilcox.test(rnorm(10), exact=TRUE, correct=FALSE) > > > > From all of these I am pretty sure the 1st one is likely unintended, > > so attaching a small patch to adjust it. Can also try patching others if > > consensus is reached that the behavioiur has to be modified. > > > Kind regards, > > Karolis Koncevi?ius. > > > --- > > > Index: wilcox.test.R > > ==================================================================> > --- wilcox.test.R (revision 77540) > > +++ wilcox.test.R (working copy) > > @@ -42,7 +42,7 @@ > > if(paired) { > > if(length(x) != length(y)) > > stop("'x' and 'y' must have the same length") > > - OK <- complete.cases(x, y) > > + OK <- is.finite(x) & is.finite(y) > > x <- x[OK] - y[OK] > > y <- NULL > > } > > > ______________________________________________ > > R-devel at r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-devel
So I tried adding Infinity support for all cases.
And it is (as could be expected) more complicated than I thought.
It is easy to add Inf support for the test. The problems start with
conf.int=TRUE.
Currently confidence intervals are computed via `uniroot()` and, in the
case of infinities, we are computationally looking for roots over
infinite interval which results in an error. I suspect this is the
reason Inf values were removed in the first place.
Just a note, I found a few more errors/inconsistencies when requesting
confidence intervals with paired=TRUE (due to Infinities being left in).
Current error in Inf-Inf scenario:
wilcox.test(c(1,2,Inf), c(4,8,Inf), paired=TRUE, conf.int=TRUE)
Error in if (ZEROES) x <- x[x != 0] :
missing value where TRUE/FALSE needed
NaN confidence intervals:
wilcox.test(c(1:9,Inf), c(21:28,Inf,30), paired=TRUE, conf.int=TRUE)
Wilcoxon signed rank test with continuity correction
data: c(1:9, Inf) and c(21:28, Inf, 30)
V = 9.5, p-value = 0.0586
alternative hypothesis: true location shift is not equal to 0
0 percent confidence interval:
NaN NaN
sample estimates:
midrange
NaN
The easiest "fix" for consistency would be to simply remove Infinity
support from the paired=TRUE case.
But going with the more desirable approach of adding Infinity support
for non-paired cases - it is currently not clear to me what confidence
intervals and pseudomedian should be. Specially when Infinities are on
both sides.
Regards,
Karolis Koncevi?ius.
On 2019-12-07 23:18, Karolis Koncevi?ius wrote:>Thank you for a fast response. Nice to see this mailing list being so
>alive.
>
>Regarding Inf issue: I agree with your assessment that Inf should not
>be removed. The code gave me an impression that Inf values were
>intentionally removed (since is.finite() was used everywhere, except
>for paired case). I will try to adjust my patch according to your
>feedback.
>
>One more thing: it seems like you assumed that issues 2:4 are all
>related to machine precision, which is not the case - only 2nd issue
>is.
>Just wanted to draw this to your attention, in case you might have
>some feedback and guidelines about issues 3 and 4 as well.
>
>
>
>On 2019-12-07 21:59, Martin Maechler wrote:
>>>>>>>Karolis Koncevi?ius
>>>>>>> on Sat, 7 Dec 2019 20:55:36 +0200 writes:
>>
>> > Hello,
>> > Writing to share some things I've found about wilcox.test()
that seem a
>> > a bit inconsistent.
>>
>> > 1. Inf values are not removed if paired=TRUE
>>
>> > # returns different results (Inf is removed):
>> > wilcox.test(c(1,2,3,4), c(0,9,8,7))
>> > wilcox.test(c(1,2,3,4), c(0,9,8,Inf))
>>
>> > # returns the same result (Inf is left as value with highest
rank):
>> > wilcox.test(c(1,2,3,4), c(0,9,8,7), paired=TRUE)
>> > wilcox.test(c(1,2,3,4), c(0,9,8,Inf), paired=TRUE)
>>
>>Now which of the two cases do you consider correct ?
>>
>>IHMO, the 2nd one is correct: it is exactly one property of the
>>*robustness* of the wilcoxon test and very desirable that any
>>(positive) outlier is treated the same as just "the largest
>>value" and the test statistic (and hence the p-value) should not
>>change.
>>
>>So I think the first case is wrong, notably if modified, (such
>>that the last y is the overall maximal value (slightly larger sample):
>>
>>>wilcox.test(1:7, 1/8+ c(9:4, 12))
>>
>> Wilcoxon rank sum test
>>
>>data: 1:7 and 1/8 + c(9:4, 12)
>>W = 6, p-value = 0.01748
>>alternative hypothesis: true location shift is not equal to 0
>>
>>>wilcox.test(1:7, 1/8+ c(9:4, 10000))
>>
>> Wilcoxon rank sum test
>>
>>data: 1:7 and 1/8 + c(9:4, 10000)
>>W = 6, p-value = 0.01748
>>alternative hypothesis: true location shift is not equal to 0
>>
>>>wilcox.test(1:7, 1/8+ c(9:4, Inf))
>>
>> Wilcoxon rank sum test
>>
>>data: 1:7 and 1/8 + c(9:4, Inf)
>>W = 6, p-value = 0.03497
>>alternative hypothesis: true location shift is not equal to 0
>>
>>The Inf case should definitely give the same as the 10'000 case.
>>That's exactly one property of a robust statistic.
>>
>>Thank you, Karolis, this is pretty embarrassing to only be detected now
after 25+ years of R in use ...
>>
>>The correct fix starts with replacing the is.finite() by !is.na()
and keep the 'Inf' in the rank computations...
>>(but then probably also deal with the case of more than one Inf, notably
the Inf - Inf "exception" which is not triggered by your example...)
>>
>>
>>---
>>
>>Ben addressed the "rounding" / numerical issues unavoidable
for
>>the other problems.
>>
>> > 2. tolerance issues with paired=TRUE.
>>
>> > wilcox.test(c(4, 3, 2), c(3, 2, 1), paired=TRUE)
>> > # ...
>> > # Warning: cannot compute exact p-value with ties
>>
>> > wilcox.test(c(0.4,0.3,0.2), c(0.3,0.2,0.1), paired=TRUE)
>> > # ...
>> > # no warning
>>
>> > 3. Always 'x observations are missing' when paired=TRUE
>>
>> > wilcox.test(c(1,2), c(NA_integer_,NA_integer_), paired=TRUE)
>> > # ...
>> > # Error: not enough (finite) 'x' observations
>>
>> > 4. No indication if normal approximation was used:
>>
>> > # different numbers, but same "method" name
>> > wilcox.test(rnorm(10), exact=FALSE, correct=FALSE)
>> > wilcox.test(rnorm(10), exact=TRUE, correct=FALSE)
>>
>>
>> > From all of these I am pretty sure the 1st one is likely
unintended,
>> > so attaching a small patch to adjust it. Can also try patching
others if
>> > consensus is reached that the behavioiur has to be modified.
>>
>> > Kind regards,
>> > Karolis Koncevi?ius.
>>
>> > ---
>>
>> > Index: wilcox.test.R
>> >
==================================================================>>
> --- wilcox.test.R (revision 77540)
>> > +++ wilcox.test.R (working copy)
>> > @@ -42,7 +42,7 @@
>> > if(paired) {
>> > if(length(x) != length(y))
>> > stop("'x' and 'y' must have the same
length")
>> > - OK <- complete.cases(x, y)
>> > + OK <- is.finite(x) & is.finite(y)
>> > x <- x[OK] - y[OK]
>> > y <- NULL
>> > }
>>
>> > ______________________________________________
>> > R-devel at r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-devel
>>>>> Karolis Koncevi?ius >>>>> on Mon, 9 Dec 2019 23:43:36 +0200 writes:> So I tried adding Infinity support for all cases. > And it is (as could be expected) more complicated than I thought. "Of course !" Thank you, Karolis, in any case! > It is easy to add Inf support for the test. The problems start with conf.int=TRUE. > Currently confidence intervals are computed via `uniroot()` and, in the > case of infinities, we are computationally looking for roots over > infinite interval which results in an error. I suspect this is the > reason Inf values were removed in the first place. Maybe. It's still wrong to be done "up front". I'm sure 98% (or so ;-) of all calls to wilcox.test() do *not* use conf.int = TRUE > Just a note, I found a few more errors/inconsistencies when requesting > confidence intervals with paired=TRUE (due to Infinities being left in). > Current error in Inf-Inf scenario: > wilcox.test(c(1,2,Inf), c(4,8,Inf), paired=TRUE, conf.int=TRUE) > Error in if (ZEROES) x <- x[x != 0] : > missing value where TRUE/FALSE needed Good catch .. notably as it also happens when conf.int=FALSE as by default. My version of wilcox.test() now does give the same as when the to 'Inf' are left away. > NaN confidence intervals: > wilcox.test(c(1:9,Inf), c(21:28,Inf,30), paired=TRUE, conf.int=TRUE) > Wilcoxon signed rank test with continuity correction > data: c(1:9, Inf) and c(21:28, Inf, 30) > V = 9.5, p-value = 0.0586 > alternative hypothesis: true location shift is not equal to 0 > 0 percent confidence interval: > NaN NaN > sample estimates: > midrange > NaN I don't see a big problem here. The NaN's in some sense show the best that can be computed with this data. Statistic and P-value, but no conf.int. > The easiest "fix" for consistency would be to simply remove Infinity > support from the paired=TRUE case. I strongly disagree. We are not pruning good functionality just for some definition of consistency. > But going with the more desirable approach of adding Infinity support > for non-paired cases - it is currently not clear to me what confidence > intervals and pseudomedian should be. Specially when Infinities are on > both sides. I deem that not to be a big deal. They are not defined given the default formulas and that is reflected by NA / NaN in those parts of the result. > Regards, > Karolis Koncevi?ius. But I have also spent a few hours now on wilcox.test.default() behavior notably also looking at the "rounding" / "machine precision" situation, and also on your remark that the 'method: ...' does not indicate well enough what was computed. In my (not yet committed) but hereby proposed enhancement of wilcox.test(), I have a new argument, 'digits.rank = Inf' (the default 'Inf' corresponding to the current behavior) with help page documentation: digits.rank: a number; if finite, ?rank(signif(r, digits.rank))? will be used to compute ranks for the test statistic instead of (the default) ?rank(r)?. and then in 'Details :' For stability reasons, it may be advisable to use rounded data or to set ?digits.rank = 7?, say, such that determination of ties does not depend on very small numeric differences (see the example). and then in 'Examples: ' ## accuracy in ties determination via 'digits.rank': wilcox.test( 4:2, 3:1, paired=TRUE) # Warning: cannot compute exact p-value with ties wilcox.test((4:2)/10, (3:1)/10, paired=TRUE) # no ties => *no* warning wilcox.test((4:2)/10, (3:1)/10, paired=TRUE, digits.rank = 9) # same ties as (4:2, 3:1) ---------------------- Lastly, I propose to replace "test" by "exact test" in the 'method' component (and print out) in case exact computations were used. This information should be part of the returned "htest" object, and not only visible from the arguments and warnings that are printed during the computations. This last change is in some sense the "most back-incompatible" change of these, because many wilcox.test() printouts would slightly change, e.g., > w0 <- wilcox.test( 1:5, 4*(0:4), paired=TRUE) Wilcoxon signed rank exact test data: 1:5 and 4 * (0:4) V = 1, p-value = 0.125 alternative hypothesis: true location shift is not equal to 0 where before (in R <= 3.6.x) it is just Wilcoxon signed rank test data: ......... ............... ............... but I think R 4.0.0 is a good occasion for such a change. Constructive feedback on all this is very welcome! Martin > On 2019-12-07 23:18, Karolis Koncevi?ius wrote: >> Thank you for a fast response. Nice to see this mailing list being so >> alive. >> >> Regarding Inf issue: I agree with your assessment that Inf should not >> be removed. The code gave me an impression that Inf values were >> intentionally removed (since is.finite() was used everywhere, except >> for paired case). I will try to adjust my patch according to your >> feedback. >> >> One more thing: it seems like you assumed that issues 2:4 are all >> related to machine precision, which is not the case - only 2nd issue >> is. >> Just wanted to draw this to your attention, in case you might have >> some feedback and guidelines about issues 3 and 4 as well. >> >> >> >> On 2019-12-07 21:59, Martin Maechler wrote: >>>>>>>> Karolis Koncevi?ius >>>>>>>> on Sat, 7 Dec 2019 20:55:36 +0200 writes: >>> >>> > Hello, >>> > Writing to share some things I've found about wilcox.test() that seem a >>> > a bit inconsistent. >>> >>> > 1. Inf values are not removed if paired=TRUE >>> >>> > # returns different results (Inf is removed): >>> > wilcox.test(c(1,2,3,4), c(0,9,8,7)) >>> > wilcox.test(c(1,2,3,4), c(0,9,8,Inf)) >>> >>> > # returns the same result (Inf is left as value with highest rank): >>> > wilcox.test(c(1,2,3,4), c(0,9,8,7), paired=TRUE) >>> > wilcox.test(c(1,2,3,4), c(0,9,8,Inf), paired=TRUE) >>> >>> Now which of the two cases do you consider correct ? >>> >>> IHMO, the 2nd one is correct: it is exactly one property of the >>> *robustness* of the wilcoxon test and very desirable that any >>> (positive) outlier is treated the same as just "the largest >>> value" and the test statistic (and hence the p-value) should not >>> change. >>> >>> So I think the first case is wrong, notably if modified, (such >>> that the last y is the overall maximal value (slightly larger sample): >>> >>>> wilcox.test(1:7, 1/8+ c(9:4, 12)) >>> >>> Wilcoxon rank sum test >>> >>> data: 1:7 and 1/8 + c(9:4, 12) >>> W = 6, p-value = 0.01748 >>> alternative hypothesis: true location shift is not equal to 0 >>> >>>> wilcox.test(1:7, 1/8+ c(9:4, 10000)) >>> >>> Wilcoxon rank sum test >>> >>> data: 1:7 and 1/8 + c(9:4, 10000) >>> W = 6, p-value = 0.01748 >>> alternative hypothesis: true location shift is not equal to 0 >>> >>>> wilcox.test(1:7, 1/8+ c(9:4, Inf)) >>> >>> Wilcoxon rank sum test >>> >>> data: 1:7 and 1/8 + c(9:4, Inf) >>> W = 6, p-value = 0.03497 >>> alternative hypothesis: true location shift is not equal to 0 >>> >>> The Inf case should definitely give the same as the 10'000 case. >>> That's exactly one property of a robust statistic. >>> >>> Thank you, Karolis, this is pretty embarrassing to only be detected now after 25+ years of R in use ... >>> >>> The correct fix starts with replacing the is.finite() by !is.na() and keep the 'Inf' in the rank computations... >>> (but then probably also deal with the case of more than one Inf, notably the Inf - Inf "exception" which is not triggered by your example...) >>> >>> >>> --- >>> >>> Ben addressed the "rounding" / numerical issues unavoidable for >>> the other problems. >>> >>> > 2. tolerance issues with paired=TRUE. >>> >>> > wilcox.test(c(4, 3, 2), c(3, 2, 1), paired=TRUE) >>> > # ... >>> > # Warning: cannot compute exact p-value with ties >>> >>> > wilcox.test(c(0.4,0.3,0.2), c(0.3,0.2,0.1), paired=TRUE) >>> > # ... >>> > # no warning >>> >>> > 3. Always 'x observations are missing' when paired=TRUE >>> >>> > wilcox.test(c(1,2), c(NA_integer_,NA_integer_), paired=TRUE) >>> > # ... >>> > # Error: not enough (finite) 'x' observations >>> >>> > 4. No indication if normal approximation was used: >>> >>> > # different numbers, but same "method" name >>> > wilcox.test(rnorm(10), exact=FALSE, correct=FALSE) >>> > wilcox.test(rnorm(10), exact=TRUE, correct=FALSE) >>> >>> >>> > From all of these I am pretty sure the 1st one is likely unintended, >>> > so attaching a small patch to adjust it. Can also try patching others if >>> > consensus is reached that the behavioiur has to be modified. >>> >>> > Kind regards, >>> > Karolis Koncevi?ius. >>> >>> > --- >>> >>> > Index: wilcox.test.R >>> > ================================================================== >>> > --- wilcox.test.R (revision 77540) >>> > +++ wilcox.test.R (working copy) >>> > @@ -42,7 +42,7 @@ >>> > if(paired) { >>> > if(length(x) != length(y)) >>> > stop("'x' and 'y' must have the same length") >>> > - OK <- complete.cases(x, y) >>> > + OK <- is.finite(x) & is.finite(y) >>> > x <- x[OK] - y[OK] >>> > y <- NULL >>> > } >>> >>> > ______________________________________________ >>> > R-devel at r-project.org mailing list >>> > https://stat.ethz.ch/mailman/listinfo/r-devel