Hi:
On Tue, Oct 12, 2010 at 12:51 AM, Stratos Laskarides<stratl...@gmail.com
wrote:
Dear Madam/Sir
This may be quite a long shot...
By way of intro, I am a masters student in actuarial science at the
University of Cape Town, and I am doing a project in R on some healthcare
cost data. During my coding in R I encountered an error message, which I
then googled, but I am still unable to resolve the issue.
I would like to please ask if and how it is possible to resolve the
problem
raised by the error message "Error: NA/NaN/Inf in foreign function call
(arg
1) In addition: Warning message: *step size truncated due to divergence"
*in
R?
That error message can arise if division by zero occurs somewhere in the
computation. Try using ftable() or some related function that will print
out your
complete table (4-way?) and check whether you have zero frequency in one
or more cells. If there are zero frequencies, that does not necessarily
explain
the problem, but it's a reasonable initial hypothesis. Merging some
categories to
get enough frequencies per cell may be useful if you do have zero
frequencies,
and then try the fit again to see if you get more sensible results.
When the error is thrown, it can be useful to do
traceback()
as it recalls the sequence of function calls that led up to the error, but
it helps to
have enough R experience to make heads or tails of the output :)
As for some background on my specific data and research problem at hand, I
am fitting a gamma regression model to 13 000 lines of insurance claims
data, which will be regressed against categorical variables such as Age
Band, Gender, and Region.
The more variables you have in the model, the greater the number of cell
combinations. A 15 x 2 x 5 combination of your three variables, for
example, would generate 150 combinations of the three variables, and it's
entirely possible for a few of those combinations to have small or zero
frequencies.
In addition, adding a new variable to the model would at least double the
number
of cells, spreading/thinning out the data even more.
Perhaps my problem arises because the data set is too large and the
iteratively reweighted least squares algorithm therefore cannot converge,
in
which case I perhaps need another GLM type. Or maybe the categorical
explanatory variables can take on too many values (e.g. there are 15 Age
Bands, 5 Regions).
If your response is continuous and positive valued with a right skewed
distribution,
then a Gamma model would appear to be sensible.
The data set is not too large; successful GLMs have been fit with much
larger
data sets. Your second hypothesis sounds more plausible, though.
HTH,
Dennis
Any insights you could provide would be much appreciated.
Thank you ever so much.
Kind regards
Stratos Laskarides
South Africa
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
and provide commented, minimal, self-contained, reproducible code.
