k.jewell at campden.co.uk
2008-Feb-15 16:47 UTC
[R] Behaviour of integrate (was 'Poisson-lognormal probability calcul ations')
Hi again,
Adding further information to my own query, this function gets to the core
of the problem, which I think lies in the behaviour of 'integrate'.
-------------------------------------
function (x, meanlog = 0, sdlog = 1, ...) {
require(stats)
integrand <- function(t, x, meanlog, sdlog) dpois(x,t)*dlnorm(t,
meanlog, sdlog)
mapply(function(x, meanlog, sdlog, ...)
# (1/gamma(x+1))*
integrate(function(t, x, meanlog, sdlog)
# gamma(x+1)*
integrand(t, x, meanlog, sdlog),
lower = 0, upper = Inf, x = x, meanlog = meanlog, sdlog sdlog,
...)$value,
x, meanlog, sdlog, ...
)
}
----------------------------------------
Mathematically, the presence or not of the two commented lines should make
no difference; they multiply the integrand by a constant (with respect to
the integration), then divide the result by the same constant. In practice
they make a big difference! I guess they're altering the behaviour of the
'integrate'.
I'd have thought the presence of the lines would worsen the behaviour.
Without the lines the integrand is reasonably small, the integral is < 1.
With the lines the limit on the integral is x!, leading to "non-finite
function values" for x much > 170, even if we use logs to get around the
limit on gamma(x).
In fact with the lines the plot of function(x) v. x looks reasonable (but I
don't know if the values are correct!!), but without the lines it looks
silly, I just don't believe it!
I thought the problem might relate to the note in ?integrate "If the
function is approximately constant (in particular, zero) over nearly all its
range it is possible that the result and error estimate may be seriously
wrong.". I wouldn't really expect multiplication by a large constant to
fix
such errors (??), but the lognormal distribution is skew so it might be
considered "approximately constant ... over nearly all its range".
Even
though ...> integrate(dlnorm, 0, Inf)
1 with absolute error < 2.5e-07
... suggests that this is not the source of the problem, I tried changing
variables to integrate over a normally distributed variable:
----------------
function (x, meanlog = 0, sdlog = 1, ...) {
require(stats)
integrand <- function(t, x, meanlog, sdlog) dpois(x,exp(t))*dnorm(t,
meanlog, sdlog)
mapply(function(x, meanlog, sdlog, ...)
# (1/gamma(x+1))*
integrate(function(t, x, meanlog, sdlog)
# gamma(x+1)*
integrand(t, x, meanlog, sdlog),
lower = -Inf, upper = Inf, x = x, meanlog = meanlog, sdlog sdlog,
...)$value,
x, meanlog, sdlog, ...
)
}
-----------------------------
Still no better; with the constants the values look reasonably smooth (but
are they correct??), without the constants the values are silly.
I've tried reducing rel.tol and increasing subdivisions, they change the
behaviour a little but don't "fix" it, and I still get the marked
difference
between the presence and absence of those lines (and I'm increasingly unsure
whether either answer is correct!).
I'm still trying. but I really think I'm going nowhere. Has anyone any
ideas?
Thanks in advance,
Keith Jewell
mailto:k.jewell at campden.co.uk telephone (direct) +44 (0)1386
842055> -----Original Message-----
> From: Jewell, Keith
> Sent: 15 February 2008 11:16
> To: 'r-help at r-project.org'
> Subject: Poisson-lognormal probability calculations
>
> Hi,
>
> just for the record, although I don't think it's relevant (!)
> -------------------------------------
> > sessionInfo()
> R version 2.6.0 (2007-10-03)
> i386-pc-mingw32
>
> locale:
> LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United
> Kingdom.1252;LC_MONETARY=English_United
> Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252
>
> attached base packages:
> [1] stats4 splines stats graphics grDevices utils
> datasets
> methods base
>
> other attached packages:
> [1] VGAM_0.7-5 xlsReadWrite_1.3.2
> --------------------------------
>
> I'm having some problems with a Poisson-lognormal density (mass?)
> function.
>
> VGAM has the dpolono function, but that doesn't work for x-values over
> 170, and I need to go to *much* bigger numbers. It fails first because
> of gamma overflow, then because of non-finite integrand.
> ---------------------
> > VGAM::dpolono(170)
> [1] 4.808781e-09
> > VGAM::dpolono(171)
> [1] 0
> Warning message:
> In VGAM::dpolono(171) : value out of range in 'gammafn'
> > VGAM::dpolono(172)
> [1] 0
> Warning message:
> In VGAM::dpolono(172) : value out of range in 'gammafn'
> > VGAM::dpolono(173)
> [1] 0
> Warning message:
> In VGAM::dpolono(173) : value out of range in 'gammafn'
> > VGAM::dpolono(174)
> Error in integrate(f = integrand, lower = -Inf, upper = Inf, x = x[i],
> :
> non-finite function value
> -------------
>
> I tidied up a little (to my eyes only, no offence intended to the
> estimable authors of VGAM) and avoided the gamma overflow by using logs
> - OK so far, it agrees almost perfectly with VGAM::dpolono for x up to
> 170, and now extends the range up to 173 (wow!!).
> -------------
> dApolono <-
> function (x, meanlog = 0, sdlog = 1, ...) {
> require(stats)
> integrand <- function(t, x, meanlog, sdlog) exp(-t+x*log(t)-
> log(sdlog*t*sqrt(2*pi))-0.5*((log(t)-meanlog)/sdlog)^2)
> mapply(function(x, meanlog, sdlog, ...){
> temp <- try(
> integrate(f = integrand, lower = 0, upper = Inf, x = x,
> meanlog = meanlog, sdlog = sdlog, ...)
> )
> ifelse(inherits(temp, "try-error"), NA,
exp(log(temp$value)-
> lgamma(x+1)))
> }
> , x, meanlog, sdlog, ...
> )
> }
> plot(log(dApolono(0:173)))
> dApolono(174)
> -----------------------------
>
> Addressing the non-finite integrand, I noticed that the gamma(x+1)
> divisor was outside the integrand so that as x gets bigger the
> integrand gets bigger, only to be finally divided by an increasing
> gamma(x+1). I reasoned that putting the divisor inside the integral
> might incur a substantial performance hit, but would keep the integrand
> at reasonable values.
>
> --------------------------------------
> dApolono <-
> function (x, meanlog = 0, sdlog = 1, ...) {
> require(stats)
> integrand <- function(t, x, meanlog, sdlog) exp(-t+x*log(t)-
> log(sdlog*t*sqrt(2*pi))-0.5*((log(t)-meanlog)/sdlog)^2-lgamma(x+1))
> mapply(function(x, meanlog, sdlog, ...){
> temp <- try(
> integrate(f = integrand, lower = 0, upper = Inf, x = x,
> meanlog = meanlog, sdlog = sdlog, ...)
> )
> ifelse(inherits(temp, "try-error"), NA, temp$value)
> }
> , x, meanlog, sdlog, ...
> )
> }
> plot(log(dApolono(0:173)))
> dApolono(174)
> dApolono(1E3)
> -------------------------
>
> This avoids the non-finite integrand and gives me answers at much
> higher x, but now at least some of the answers are wrong (not to say
> silly), even in the range where the other versions worked. I've tried
> other variations including changing the variable of integration to
> log(t) and integrating dpois()*dlnorm(), but I can't fix it; if the
> factorial (=gamma) is inside the integrand I get silly answers, if it's
> outside I get non-finite integrand.
>
> I'm tearing my hair. Can anyone suggest where I may be going wrong? Any
> suggestions at all will be appreciated.
>
> Thanks in advance,
>
> Keith Jewell
> mailto:k.jewell at campden.co.uk telephone (direct) +44 (0)1386 842055
>
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