[R] Looking for greater floating-point precision

[Gabor Grothendieck]

You might use the idea below together with sum.exact in the caTools package.

On 11/17/06, Martin Maechler <maechler at stat.math.ethz.ch> wrote:
> >>>>> "Paul" == Paul Smith <phhs80 at gmail.com>
> >>>>>     on Fri, 17 Nov 2006 12:12:52 +0000 writes:
>
>    Paul> On 11/16/06, Prof Brian Ripley <ripley at stats.ox.ac.uk>
>    Paul> wrote:
>    >> > For my calculations, I am needing to use more
>    >> floating-point precision > than the default one of R. Is
>    >> that possible? And, if yes, how?
>    >>
>    >> See package gmp (but that will be slow and cumbersome for
>    >> all but simple calculations).
>    >>
>    >> The real issue is that R already uses the maximum
>    >> precision of the FPU for many common FPUs (but not all).
>    >> Since you have forgotten to tell us anything about your
>    >> environment we don't know if that applies: there may be
>    >> compiler options you can use to raise the precision.
>    >>
>    >> Please do study the posting guide: we are surprisingly
>    >> good at mind-reading, but prefer to be told exactly what
>    >> you want to do with R in what environment and why you are
>    >> 'needing' something.
>
>    Paul> Thanks, Gabor and Prof. Ripley. After some research, I
>    Paul> conclude that the problem occurring to me cannot be
>    Paul> removed for any finite floating-point precision. (I do
>    Paul> need infinite floating-point precision.) The
>    Paul> problematic operation is the successive multiplication
>    Paul> of reals between 0 and 1; after a certain number of
>    Paul> multiplications, significant rounding errors occur.
>
> of course.  But that's a very well known and common problem in
> several areas of applied probability and statistics.
>
> AFAIK, in most cases the "obvious" remedy is the following:
> Instead of multiplying probabilities,
> you add log-probabilities and only exp()onentiate at the end {if
> needed at all}.  This also applies if your numbers in [0,1] are
> not probabilities per se.
>
> Note that R makes it particularly efficient to work with
> log-probabilities, because all  d<foo>() and p<foo> functions
> have a 'log' or 'log.p' argument which return log-values
> already, often with much more precision and efficiency than if
> you'd take the log of the probabilities yourself.
>
> Regards,
> Martin Maechler, ETH Zurich
>
>
>    Paul> I did read the posting guide, but I could not
>    Paul> anticipate the relevance of indicating the environment
>    Paul> that I am using: Fedora Core 6 (Linux) running on a
>    Paul> Pentium Dual Core and R 2.4.0.
>
>    Paul> Paul
>
>    Paul> ______________________________________________
>    Paul> R-help at stat.math.ethz.ch mailing list
>    Paul> https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do
>    Paul> read the posting guide
>    Paul> http://www.R-project.org/posting-guide.html and
>    Paul> provide commented, minimal, self-contained,
>    Paul> reproducible code.
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>