# [Rd] 0.5 != integrate(dnorm,0,20000) = 0

John Nolan jpnolan at american.edu
Tue Dec 7 16:02:06 CET 2010

```Putting in Inf for the upper bound does not work in general:
all 3 of the following should give 0.5

> integrate( dnorm, 0, Inf )
0.5 with absolute error < 4.7e-05

> integrate( dnorm, 0, Inf, sd=100000 )
Error in integrate(dnorm, 0, Inf, sd = 1e+05) :
the integral is probably divergent

> integrate( dnorm, 0, Inf, sd=10000000 )
5.570087e-05 with absolute error < 0.00010

Numerical quadrature methods look at a finite number of
points, and you can find examples that will confuse any
algorithm.  Rather than hope a general method will solve
all problems, users should look at their integrand and
pick an appropriate region of integration.

John Nolan, American U.

-----r-devel-bounces at r-project.org wrote: -----
To: r-devel at r-project.org
From: Pierre Chausse
Sent by: r-devel-bounces at r-project.org
Date: 12/07/2010 09:46AM
Subject: Re: [Rd] 0.5 != integrate(dnorm,0,20000) = 0

The warning about "absolute error == 0" would not be sufficient
because if you do
> integrate(dnorm, 0, 5000)
2.326323e-06 with absolute error < 4.6e-06

We get reasonable absolute error and wrong answer. For very high upper
bound, it seems more stable to use "Inf". In that case, another
.External is used which seems to be optimized for high or low bounds:

> integrate(dnorm, 0,Inf)
0.5 with absolute error < 4.7e-05

On 10-12-07 8:38 AM, John Nolan wrote:
> I have wrestled with this problem before.  I think correcting
> the warning to "absolute error ~<= 0" is a good idea, and printing
> a warning if subdivisions==1 is also helpful.  Also, including
> a simple example like the one that started this thread on the
> help page for integrate might make the issue more clear to users.
>
> But min.subdivisions is probably not.  On the example with dnorm( ),
> I doubt 3 subdivisions would work.  The problem isn't that
> we aren't sudividing enough, the problem is that the integrand
> is 0 (in double precision) on most of the region and the
> algorithm isn't designed to handle this.  There is no way to
> determine how many subdivisions are necessary to get a reasonable
> answer without a detailed analysis of the integrand.
>
> I've gotten useful results with integrands that are monotonic on
> the tail with a "self-triming integration" routine
> like the following:
>
>> right.trimmed.integrate<- function( f, lower, upper, epsilon=1e-100, min.width=1e-10, ... ) {
> + # trim the region of integration on the right until f(x)>  epsilon
> +
> + a<- lower; b<- upper
> + while ( (b-a>min.width)&&  (f(b)<epsilon) ) { b<- (a+b)/2 }
> +
> + return( integrate(f,a,b,...) ) }
>
>> right.trimmed.integrate( dnorm, 0, 20000 )  # test
> 0.5 with absolute error<  9.2e-05
>
> This can be adapted to left trim or (left and right) trim, abs(f(x)-c)>epsilon,
> etc.  Setting the tolerances epsilon and min.width is an issue,
> but an explicit discussion of these values could encourage people to
> think about the problem in their specific case.  And of course, none
> of this guarantees a correct answer, especially if someone tries this
> on non-monotonic integrands with complicated 0 sets.  One could write
> a somewhat more user-friendly version where the user has to specify
> some property (or set of properties) of the integrand, e.g. "right-tail
> decreasing to 0", etc. and have the algorithm try to do smart
> trimming based on this.  But perhaps this getting too involved.
>
> In the end, there is no general solution because any solution
> depends on the specific nature of the integrand.  Clearer messages,
> warnings in suspicious cases like subdivisions==1, and a simple
> example explaining what the issue is in the help page would help
> some people.
>
> John
>
>   ...........................................................................
>
>   John P. Nolan
>   Math/Stat Department
>   227 Gray Hall
>   American University
>   4400 Massachusetts Avenue, NW
>   Washington, DC 20016-8050
>
>   jpnolan at american.edu
>   202.885.3140 voice
>   202.885.3155 fax
>   ...........................................................................
>
> -----r-devel-bounces at r-project.org wrote: -----
> To: r-devel at r-project.org, Prof Brian Ripley<ripley at stats.ox.ac.uk>
> From: Martin Maechler
> Sent by: r-devel-bounces at r-project.org
> Date: 12/07/2010 03:29AM
> Subject: Re: [Rd] 0.5 != integrate(dnorm,0,20000) = 0
>
>>>>>> Prof Brian Ripley<ripley at stats.ox.ac.uk>
>>>>>>      on Tue, 7 Dec 2010 07:41:16 +0000 (GMT) writes:
>      >  On Mon, 6 Dec 2010, Spencer Graves wrote:
>      >>  Hello:
>      >>
>      >>
>      >>  The example "integrate(dnorm,0,20000)" says it "fails on many systems".
>      >>  I just got 0 from it, when I should have gotten either an error or something
>      >>  close to 0.5.  I got this with R 2.12.0 under both Windows Vista_x64 and
>      >>  Linux (Fedora 13);  see the results from Windows below.  I thought you might
>      >>  want to know.
>
>      >  Well, isn't that exactly what the help page says happens?  That
>      >  example is part of a section entitled
>
>      >  ## integrate can fail if misused
>
>      >  and is part of the illustration of
>
>      >  If the function is
>      >  approximately constant (in particular, zero) over nearly all its
>      >  range it is possible that the result and error estimate may be
>      >  seriously wrong.
>
> yes, of course,
> and the issue has been known for ``ages''  ..
> ..........
> ..........
> but it seems that too many useRs are not reading the help
> page carefully, but only browse it quickly.
> I think we (R developers) have to live with this fact
> and should consider adapting to it a bit more, particularly in
> this case (see below)
>
>      >>
>      >>  Thanks for all your work in creating and maintaining R.
>      >>
>      >>
>      >>  Best Wishes,
>      >>  Spencer Graves
>      >>  ###############################
>
>      >>
>      >>  integrate(dnorm,0,20000) ## fails on many systems
>
>      >>  0 with absolute error<  0
>
> and this is particularly unsatisfactory for another reason:
>
> "absolute error<  0"
> is *always* incorrect, so I think we should change *some*thing.
>
> We could just use "<=" (and probably should in any case, or
> "<  ~= x" which would convey ``is less than about x'' which I
> think is all we can say),
> but could consider giving a different message when the integral
> evaluates to 0 or, rather actually,
> only when the error bound ('abs.error') is 0 *and* 'subdivisions == 1'
> as the latter indicates that the algorithm treated the integrand
> f(.) as if f() was a linear function.
>
> But in my quick experiments, even for linear (incl. constant)
> functions, the 'abs.error' returned is never 0.
>
> If we want to be cautious,
> such a warning could be made explicitly suppressable by an argument
>        .warn.if.doubtful = TRUE
>
> An additional possibility I'd like to try, is a new argument
>     'min.subdivisions = 3' which specifies the *minimal* number
> of subdivisions to be used in addition to the already present
>     'subdivisions = 100' (= the maximum number of subintervals.)
>
> Martin Maechler,
> ETH Zurich
>
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>

--
*Pierre Chaussé*
Assistant Professor
Department of Economics
University of Waterloo

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