I'd compute this in the log-scale (taking also advantage of the
'log'
and 'log.p' arguments of dnorm() and pnorm(), respectively), and then
transform back, e.g.,
fn1 <- function(B){
-(pnorm(B) * dnorm(B) * B + dnorm(B)^2)/pnorm(B)^2
}
fn2 <- function(B){
p1 <- dnorm(B, log = TRUE) + log(-B) - pnorm(B, log.p = TRUE)
p2 <- 2 * (dnorm(B, log = TRUE) - pnorm(B, log.p = TRUE))
exp(p1) - exp(p2)
}
fn1(c(-15, -25, -35, -55, -105))
fn2(c(-15, -25, -35, -55, -105))
I hope it helps.
Best,
Dimitris
----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(0)16/336899
Fax: +32/(0)16/337015
Web: http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm
Quoting Ott Toomet <otoomet at ut.ee>:
> Dear R-people,
>
> I have to compute
>
> C - -(pnorm(B)*dnorm(B)*B + dnorm(B)^2)/pnorm(B)^2
>
> This expression seems to be converging to -1 if B approaches to -Inf
> (although I am unable to prove it). R has no problems until B
> equals
> around -28 or less, where both numerator and denominator go to 0 and
> you get NaN. A simple workaround I did was
>
> C <- ifelse(B > -25,
> -(pnorm(B)*dnorm(B)*B + dnorm(B)^2)/pnorm(B)^2,
> -1)
>
> It works well for me (32bit intel/linux platform). But what about
> other processors/platforms/compilator options? Are there any better
> ways for finding out at which values the numerical problems start?
> Can one derive something from .Machine$double.eps (but what about
> the
> precison of dnorm and other analytic functions)?
>
> Thanks in advance,
> Ott
>
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