| log1mexp | R Documentation | 
\mathrm{log}(1 +/- \mathrm{exp}(-a))
Numerically OptimallyCompute f(a) = log(1 - exp(-a)), respectively g(x) = log(1 + exp(x)) quickly numerically accurately.
log1mexp(a, cutoff = log(2))
log1pexp(x, c0 = -37, c1 = 18, c2 = 33.3)
| a | numeric vector of positive values | 
| x | numeric vector | 
| cutoff | positive number;  | 
| c0,c1,c2 | cutoffs for  | 
f(a) == log(1 - exp(-a)) == log1p(-exp(-a)) == log(-expm1(-a))
or
g(x) == log(1 + exp(x)) == log1p(exp(x))
computed accurately and quickly
Martin Mächler (2012).
Accurately Computing \log(1-\exp(-|a|));
https://CRAN.R-project.org/package=Rmpfr/vignettes/log1mexp-note.pdf.
a <- 2^seq(-58,10, length = 256)
fExpr <- expression(
          log(1 - exp(-a)),
          log(-expm1(-a)),
          log1p(-exp(-a)),
          log1mexp(a))
names(fExpr) <- c("DEF", "expm1", "log1p", "F")
str(fa <- do.call(cbind, as.list(fExpr)))
head(fa)# expm1() works here
tail(fa)# log1p() works here
## graphically:
lwd <- 1.5*(5:2); col <- adjustcolor(1:4, 0.4)
op <- par(mfcol=c(1,2), mgp = c(1.25, .6, 0), mar = .1+c(3,2,1,1))
  matplot(a, fa, type = "l", log = "x", col=col, lwd=lwd)
  legend("topleft", fExpr, col=col, lwd=lwd, lty=1:4, bty="n")
  # expm1() & log1mexp() work here
  matplot(a, -fa, type = "l", log = "xy", col=col, lwd=lwd)
  legend("left", paste("-",fExpr), col=col, lwd=lwd, lty=1:4, bty="n")
  # log1p() & log1mexp() work here
par(op)
curve(log1pexp, -10, 10, asp=1)
abline(0,1, h=0,v=0, lty=3, col="gray")
## Cutoff c1 for log1pexp() -- not often "needed":
curve(log1p(exp(x)) - log1pexp(x), 16, 20, n=2049)
## need for *some* cutoff:
x <- seq(700, 720, by=2)
cbind(x, log1p(exp(x)), log1pexp(x))
## Cutoff c2 for log1pexp():
curve((x+exp(-x)) - x, 20, 40, n=1025)
curve((x+exp(-x)) - x, 33.1, 33.5, n=1025)
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