From wdun|@p @end|ng |rom t|bco@com  Sun Jun 23 19:34:47 2019
From: wdun|@p @end|ng |rom t|bco@com (William Dunlap)
Date: Sun, 23 Jun 2019 10:34:47 -0700
Subject: [Rd] Calculation of e^{z^2/2} for a normal deviate z
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include/Rmath.h declares a set of 'logspace' functions for use at the C
level.  I don't think there are core R functions that call them.

/* Compute the log of a sum or difference from logs of terms, i.e.,
 *
 *     log (exp (logx) + exp (logy))
 * or  log (exp (logx) - exp (logy))
 *
 * without causing overflows or throwing away too much accuracy:
 */
double  Rf_logspace_add(double logx, double logy);
double  Rf_logspace_sub(double logx, double logy);
double  Rf_logspace_sum(const double *logx, int nx);

Bill Dunlap
TIBCO Software
wdunlap tibco.com


On Sun, Jun 23, 2019 at 1:40 AM Ben Bolker <bbolker at gmail.com> wrote:

>
>   I agree with many the sentiments about the wisdom of computing very
> small p-values (although the example below may win some kind of a prize:
> I've seen people talking about p-values of the order of 10^(-2000), but
> never 10^(-(10^8)) !).  That said, there are a several tricks for
> getting more reasonable sums of very small probabilities.  The first is
> to scale the p-values by dividing the *largest* of the probabilities,
> then do the (p/sum(p)) computation, then multiply the result (I'm sure
> this is described/documented somewhere).  More generally, there are
> methods for computing sums on the log scale, e.g.
>
>
> https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.misc.logsumexp.html
>
>  I don't know where this has been implemented in the R ecosystem, but
> this sort of computation is the basis of the "Brobdingnag" package for
> operating on very large ("Brobdingnagian") and very small
> ("Lilliputian") numbers.
>
>
> On 2019-06-21 6:58 p.m., jing hua zhao wrote:
> > Hi Peter, Rui, Chrstophe and Gabriel,
> >
> > Thanks for your inputs --  the use of qnorm(., log=TRUE) is a good point
> in line with pnorm with which we devised log(p)  as
> >
> > log(2) + pnorm(-abs(z), lower.tail = TRUE, log.p = TRUE)
> >
> > that could do really really well for large z compared to Rmpfr. Maybe I
> am asking too much since
> >
> > z <-20000
> >> Rmpfr::format(2*pnorm(mpfr(-abs(z),100),lower.tail=TRUE,log.p=FALSE))
> > [1] "1.660579603192917090365313727164e-86858901"
> >
> > already gives a rarely seen small p value. I gather I also need a
> multiple precision exp() and their sum since exp(z^2/2) is also a Bayes
> Factor so I  get log(x_i )/sum_i log(x_i) instead. To this point, I am
> obliged to clarify, see
> https://statgen.github.io/gwas-credible-sets/method/locuszoom-credible-sets.pdf
> .
> >
> > I agree many feel geneticists go to far with small p values which I
> would have difficulty to argue againston the other hand it is also expected
> to see these in a non-genetic context. For instance the Framingham study
> was established in 1948 just got $34m for six years on phenotypewide
> association which we would be interesting to see.
> >
> > Best wishes,
> >
> >
> > Jing Hua
> >
> >
> > ________________________________
> > From: peter dalgaard <pdalgd at gmail.com>
> > Sent: 21 June 2019 16:24
> > To: jing hua zhao
> > Cc: Rui Barradas; r-devel at r-project.org
> > Subject: Re: [Rd] Calculation of e^{z^2/2} for a normal deviate z
> >
> > You may want to look into using the log option to qnorm
> >
> > e.g., in round figures:
> >
> >> log(1e-300)
> > [1] -690.7755
> >> qnorm(-691, log=TRUE)
> > [1] -37.05315
> >> exp(37^2/2)
> > [1] 1.881797e+297
> >> exp(-37^2/2)
> > [1] 5.314068e-298
> >
> > Notice that floating point representation cuts out at 1e+/-308 or so. If
> you want to go outside that range, you may need explicit manipulation of
> the log values. qnorm() itself seems quite happy with much smaller values:
> >
> >> qnorm(-5000, log=TRUE)
> > [1] -99.94475
> >
> > -pd
> >
> >> On 21 Jun 2019, at 17:11 , jing hua zhao <jinghuazhao at hotmail.com>
> wrote:
> >>
> >> Dear Rui,
> >>
> >> Thanks for your quick reply -- this allows me to see the bottom of
> this. I was hoping we could have a handle of those p in genmoics such as
> 1e-300 or smaller.
> >>
> >> Best wishes,
> >>
> >>
> >> Jing Hua
> >>
> >> ________________________________
> >> From: Rui Barradas <ruipbarradas at sapo.pt>
> >> Sent: 21 June 2019 15:03
> >> To: jing hua zhao; r-devel at r-project.org
> >> Subject: Re: [Rd] Calculation of e^{z^2/2} for a normal deviate z
> >>
> >> Hello,
> >>
> >> Well, try it:
> >>
> >> p <- .Machine$double.eps^seq(0.5, 1, by = 0.05)
> >> z <- qnorm(p/2)
> >>
> >> pnorm(z)
> >> # [1] 7.450581e-09 1.228888e-09 2.026908e-10 3.343152e-11 5.514145e-12
> >> # [6] 9.094947e-13 1.500107e-13 2.474254e-14 4.080996e-15 6.731134e-16
> >> #[11] 1.110223e-16
> >> p/2
> >> # [1] 7.450581e-09 1.228888e-09 2.026908e-10 3.343152e-11 5.514145e-12
> >> # [6] 9.094947e-13 1.500107e-13 2.474254e-14 4.080996e-15 6.731134e-16
> >> #[11] 1.110223e-16
> >>
> >> exp(z*z/2)
> >> # [1] 9.184907e+06 5.301421e+07 3.073154e+08 1.787931e+09 1.043417e+10
> >> # [6] 6.105491e+10 3.580873e+11 2.104460e+12 1.239008e+13 7.306423e+13
> >> #[11] 4.314798e+14
> >>
> >>
> >> p is the smallest possible such that 1 + p != 1 and I couldn't find
> >> anything to worry about.
> >>
> >>
> >> R version 3.6.0 (2019-04-26)
> >> Platform: x86_64-pc-linux-gnu (64-bit)
> >> Running under: Ubuntu 19.04
> >>
> >> Matrix products: default
> >> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0
> >> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0
> >>
> >> locale:
> >>  [1] LC_CTYPE=pt_PT.UTF-8       LC_NUMERIC=C
> >>  [3] LC_TIME=pt_PT.UTF-8        LC_COLLATE=pt_PT.UTF-8
> >>  [5] LC_MONETARY=pt_PT.UTF-8    LC_MESSAGES=pt_PT.UTF-8
> >>  [7] LC_PAPER=pt_PT.UTF-8       LC_NAME=C
> >>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
> >> [11] LC_MEASUREMENT=pt_PT.UTF-8 LC_IDENTIFICATION=C
> >>
> >> attached base packages:
> >> [1] stats     graphics  grDevices utils     datasets  methods
> >> [7] base
> >>
> >> other attached packages:
> >>
> >> [many packages loaded]
> >>
> >>
> >> Hope this helps,
> >>
> >> Rui Barradas
> >>
> >> ?s 15:24 de 21/06/19, jing hua zhao escreveu:
> >>> Dear R-developers,
> >>>
> >>> I am keen to calculate exp(z*z/2) with z=qnorm(p/2) and p is very
> small. I wonder if anyone has experience with this?
> >>>
> >>> Thanks very much in advance,
> >>>
> >>>
> >>> Jing Hua
> >>>
> >>>       [[alternative HTML version deleted]]
> >>>
> >>> ______________________________________________
> >>> R-devel at r-project.org mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-devel
> >>>
> >>
> >>        [[alternative HTML version deleted]]
> >>
> >> ______________________________________________
> >> R-devel at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-devel
> >
> > --
> > Peter Dalgaard, Professor,
> > Center for Statistics, Copenhagen Business School
> > Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> > Phone: (+45)38153501
> > Office: A 4.23
> > Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-devel at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-devel
> >
>
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
>

	[[alternative HTML version deleted]]



[Rd] Calculation of e^{z^2/2} for a normal deviate z
William Dunlap