Hi Lucas,
A similar question was raised on R-help about 10 years ago.
Take a look at David Firth's amended "quasipoisson" function here:
https://stat.ethz.ch/pipermail/r-help/2003-January/028743.html
I hope this helps
Markus
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Lucas Holland
Sent: 10 September 2013 09:53
To: r-help at R-project.org
Subject: [R] Problem fitting GAM
Hey all,
I've got some data of the form:
> head(df)
claims accident_year development_year
1 45630 1 1
2 53025 2 1
3 67318 3 1
4 93489 4 1
5 80517 5 1
6 68690 6 1
where accident_year is a factor (development_year is not).
with one entry in "claims" being negative. I'm trying to follow a
paper on claims reserving, fitting a GAM (using the GAM package) to the data
with a model of the form:
g <- gam(claims ~ s(development_year,5) + accident_year, data=df,
family=quasi(link="log", variance="mu"))
The paper specifies an over dispersed Poisson model with logarithmic link
function. It also states that the one negative value is not a problem.
However, when I run the above code I get an error:
Error in if (!(validmu(mu) && valideta(eta))) stop("Can't find
valid starting values: please specify some") :
missing value where TRUE/FALSE needed
In addition: Warning message:
In log(mu) : NaNs produced
I don't understand what exactly that means and what the problem is since in
the paper (granted, it doesn't show any implementation detail) it seems to
work fine.
If you need the complete code or any other information I'll be happy to
provide that.
Thanks!
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