I think what's been missed is that zapsmall works relative to the absolute
largest value in the vector. Hence if there's only one
item in the vector, it is the largest, so its not zapped. The function's
raison d'etre isn't to replace absolutely small values,
but small values relative to the largest. Hence a vector of similar tiny
values doesn't get zapped.
Maybe the line in the docs:
" (compared with the maximal absolute value)"
needs to read:
" (compared with the maximal absolute value in the vector)"
Barry
On Sun, Dec 17, 2023 at 2:17 PM Duncan Murdoch <murdoch.dun...@gmail.com>
wrote:
> This email originated outside the University. Check before clicking links
> or attachments.
>
> I'm really confused. Steve's example wasn't a scalar x, it was a
> vector. Your zapsmall() proposal wouldn't zap it to zero, and I don't
> see why summary() would if it was using your proposal.
>
> Duncan Murdoch
>
> On 17/12/2023 8:43 a.m., Gregory R. Warnes wrote:
> > Isn’t that the correct outcome? The user can change the number of
> digits if they want to see small values…
> >
> >
> > --
> > Change your thoughts and you change the world.
> > --Dr. Norman Vincent Peale
> >
> >> On Dec 17, 2023, at 12:11 AM, Steve Martin <stevemartin...@gmail.com>
> wrote:
> >>
> >> Zapping a vector of small numbers to zero would cause problems when
> >> printing the results of summary(). For example, if
> >> zapsmall(c(2.220446e-16, ..., 2.220446e-16)) == c(0, ..., 0) then
> >> print(summary(2.220446e-16), digits = 7) would print
> >> Min. 1st Qu. Median Mean 3rd Qu. Max.
> >> 0 0 0 0 0 0
> >>
> >> The same problem can also appear when printing the results of
> >> summary.glm() with show.residuals = TRUE if there's little dispersion
> >> in the residuals.
> >>
> >> Steve
> >>
> >>> On Sat, 16 Dec 2023 at 17:34, Gregory Warnes <g...@warnes.net> wrote:
> >>>
> >>> I was quite suprised to discover that applying `zapsmall` to a scalar
> value has no apparent effect. For example:
> >>>
> >>>> y <- 2.220446e-16
> >>>> zapsmall(y,)
> >>> [1] 2.2204e-16
> >>>
> >>> I was expecting zapsmall(x)` to act like
> >>>
> >>>> round(y, digits=getOption('digits'))
> >>> [1] 0
> >>>
> >>> Looking at the current source code, indicates that `zapsmall` is
> expecting a vector:
> >>>
> >>> zapsmall <-
> >>> function (x, digits = getOption("digits"))
> >>> {
> >>> if (length(digits) == 0L)
> >>> stop("invalid 'digits'")
> >>> if (all(ina <- is.na(x)))
> >>> return(x)
> >>> mx <- max(abs(x[!ina]))
> >>> round(x, digits = if (mx > 0) max(0L, digits -
> as.numeric(log10(mx))) else digits)
> >>> }
> >>>
> >>> If `x` is a non-zero scalar, zapsmall will never perform rounding.
> >>>
> >>> The man page simply states:
> >>> zapsmall determines a digits argument dr for calling round(x, digits =
> dr) such that values close to zero (compared with the maximal absolute
> value) are ‘zapped’, i.e., replaced by 0.
> >>>
> >>> and doesn’t provide any details about how ‘close to zero’ is defined.
> >>>
> >>> Perhaps handling the special when `x` is a scalar (or only contains a
> single non-NA value) would make sense:
> >>>
> >>> zapsmall <-
> >>> function (x, digits = getOption("digits"))
> >>> {
> >>> if (length(digits) == 0L)
> >>> stop("invalid 'digits'")
> >>> if (all(ina <- is.na(x)))
> >>> return(x)
> >>> mx <- max(abs(x[!ina]))
> >>> round(x, digits = if (mx > 0 && (length(x)-sum(ina))>1 ) max(0L,
> digits - as.numeric(log10(mx))) else digits)
> >>> }
> >>>
> >>> Yielding:
> >>>
> >>>> y <- 2.220446e-16
> >>>> zapsmall(y)
> >>> [1] 0
> >>>
> >>> Another edge case would be when all of the non-na values are the same:
> >>>
> >>>> y <- 2.220446e-16
> >>>> zapsmall(c(y,y))
> >>> [1] 2.220446e-16 2.220446e-16
> >>>
> >>> Thoughts?
> >>>
> >>>
> >>> Gregory R. Warnes, Ph.D.
> >>> g...@warnes.net
> >>> Eternity is a long time, take a friend!
> >>>
> >>>
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
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> >
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