[R] less precision, please!

[Martin Maechler]

>>>>> "Duncan" == Duncan Murdoch <murdoch at stats.uwo.ca>
>>>>>     on Fri, 09 Sep 2005 19:55:23 -0400 writes:

    Duncan> On 9/9/2005 7:41 PM, Paul MacManus wrote:
    >> I need to run qbeta on a set of 500K different parameter
    >> pairs (with a fixed quantile). For most pairs qbeta finds
    >> the solution very quickly but for a substantial minority
    >> of the cases qbeta is very slow. This occurs when the
    >> solution is very close to zero. qbeta is getting answers
    >> to a precision of about 16 decimal places. I don't need
    >> that accuracy. Is there any way to set the precision of
    >> R's calculations to, say, 9 decimal places and so speed
    >> up the whole process?
    >> 
    >> I could, of course, avoid this problem by not running
    >> qbeta when I know the solution is going to be
    >> sufficiently small but I'm more interested in ways to
    >> adjust the precision of calculations in R.

    Duncan> There's no general way to do this.  The function
    Duncan> that implements qbeta may have some tuning
    Duncan> parameters (I haven't looked), but they aren't
    Duncan> usually needed, and aren't exposed in R.

Yes.

However, I've had thoughts in the past on possibly providing such
a possibility from both R and C level.  One problem is that
``for symmetry reasons'' you would want to have this ``for all functions'' 
which would need a lot of work, for something that's really not
of too high a need.   
I agree that qbeta() can be particularly "nasty".  I'm open to
more in-depth discussion on this -- after R 2.2.0 is out

    Duncan> If you want a quick approximation, I'd suggest doing
    Duncan> your calculation on a grid of values and using
    Duncan> approx() to interpolate.

yes, or approxfun() {which prefer for its UI},
or even more smoothly  using  spline() or splinefun() {again
preferably the latter}.

One problem may be that these are only for 1-D interpolation and
qbeta() depends on three principal arguments.
Package 'akima' provides somewhat smooth 2-D interpolation.

Martin Maechler