>>>>> PIKAL Petr <petr.pi...@precheza.cz>
>>>>> on Fri, 20 Oct 2017 13:35:01 +0000 writes:
> Thank you Martin.
> If I understand correctly, OP could do
> wheat.list <- nlsList(Prop ~ SSfpl(end, A, B, xmid, scal), data=wlg)
(which is really a different model with one parameter more, and
given the relatively small sample size per group, it may not be
the best idea)
> or add some small value to all zeroes
> wlg$prop <- wlg$Prop+1e-7
> wheat.list <- nlsList(prop ~ SSlogis(end,Asym, xmid, scal), data=wlg)
> which gives fairly reasonable results.
> plot(augPred(wheat.list))
> Am I correct?
Yes, indeed, Petr, I forgot to mention that obvious workaround.
(Interestingly, he could also _subtract_ a small positive value
which is less intuitive, but work too given the code I showed.)
Martin
> Cheers
> Petr
>> -----Original Message-----
>> From: Martin Maechler [mailto:maech...@stat.math.ethz.ch]
>> Sent: Friday, October 20, 2017 1:04 PM
>> To: PIKAL Petr <petr.pi...@precheza.cz>
>> Cc: Wall, Wade A ERDC-RDE-CERL-IL CIV <wade.a.w...@erdc.dren.mil>; r-
>> h...@r-project.org
>> Subject: Re: [R] Error messages using nonlinear regression function (nls)
>>
>> >>>>> PIKAL Petr <petr.pi...@precheza.cz>
>> >>>>> on Fri, 20 Oct 2017 06:33:36 +0000 writes:
>>
>> > Hi
>> > Keep your messages in the list, you increase your chance to get some
>> answer.
>>
>> > I changed your data to groupedData object (see below), but I did not
find
>> any problem in it.
>>
>> > plot(wlg)
>> > gives reasonable picture and I am not such expert to see any problem
with
>> data. Seems to me, that something has to be wrong with nlsList function.
>>
>> >> wheat.list <- nlsList(Prop ~ SSlogis(end,Asym, xmid, scal), data=wlg)
>> > Warning message:
>> > 6 times caught the same error in lm.fit(x, y, offset = offset,
singular.ok =
>> singular.ok, ...): NA/NaN/Inf in 'x'
>>
>> > produces empty list. So maybe others could find some problem.
>>
>> > Cheers
>> > Petr
>>
>> Thank you, Petr, for the reproducible example.
>>
>> This indeed can be traced back to a bug in SSlogis() that has been there
since
>> Doug Bates added the 'nls' package on Martin's day 1999 (==> before R
version
>> 1.0.0 !) to R (svn rev 6455).
>>
>> It's this part which does contain a thinko (by whomever, only Doug may
be able
>> to remember/recall, but I guess it would have been José Pinheiro, then
the grad
>> student doing the nitty gritty), I have added 2 '----------' below to
make the 2
>> important lines stand out :
>>
>> z <- xy[["y"]]
>> ---------------------------------------------------------------------
>> if (min(z) <= 0) { z <- z - 1.05 * min(z) } # avoid zeroes
>> z <- z/(1.05 * max(z)) # scale to within unit height
>> ---------------------------------------------------------------------
>> xy[["z"]] <- log(z/(1 - z)) # logit transformation
>> aux <- coef(lm(x ~ z, xy))
>>
>> the first of the 2 lines, commented "avoid zeroes"
>> is obviously *not* avoiding zeroes in the case min(z) == 0 , and even
though the
>> 2 lines should rescale an interval [0, M] to [eps, 1 - eps] they don't
in this case.
>>
>> Consequently, the next line log(z/(1 - z)) transforms the 0s into -Inf
and these
>> lead to the warning (or error in nls())
>>
>> " NA/NaN/Inf in 'x' "
>>
>> One could fix this up by replacing min(z) by min(z, -1e-7) which may
be best
>> for pure back compatibility, but I really don't like it either :
>>
>> The famous logit - transform log( z / (1-z))
>> is really anti-symmetric around z = 1/2 , in particular should treat 0
and 1
>> "symmetrically" and I find it ugly that the previous fixup (our two
ominous
>> lines) is not at all symmetric wrt 1/2, notably the 2nd transformation
is made
>> unconditionally but the first one not.
>>
>> Fortunately, the same source file, <R>/src/library/stats/R/zzzModels.R
>> also defines the SSfpl() == 4-parameter logistic model
>>
>> and there, the 'init' function needs to do the same scaling to (0, 1)
and does it
>> much nicer, indeed (anti)symmetrically.
>>
>> I'm looking into using that in SSlogis() as well, fixing this bug.
>>
>> Martin Maechler
>> ETH Zurich and R Core Team
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