R help - Nov 2004 - Error using glm with poisson family and identity link

Hi all
I'm trying to use the function glm from the MASS package to do the 
following fit.

fit <- glm(FP ~ rand, data = tab, family = poisson(link =
"identity"),
subset = rand >= 1)
(FP is >= 0)

but I get the following error

Error: no valid set of coefficients has been found:please supply 
starting values
In addition: Warning message:
NaNs produced in: log(x)

in contrast if I fit a model without intercept

fit <- glm(FP ~ rand - 1, data = tab, family = poisson(link = 
"identity"), subset = rand >= 1)

everything goes fine.
Now my guess is that the points "naturally" have a negative intercept
so
the error is produced because I'm using the poisson distribution for the 
y and negative values are of course not admitted. Am I right?
Also if this is the cause, shouldn't the function always try to do the 
best fit given the parameters? I mean shouldn't it fit a model with 
intercept 0 anyway and report it as a bad fit?

Thanks

Federico

On Thu, 25 Nov 2004, Federico Gherardini wrote:
> Hi all
> I'm trying to use the function glm from the MASS package to do the
following
It's in the stats package.
> fit.
>
> fit <- glm(FP ~ rand, data = tab, family = poisson(link =
"identity"), subset
> = rand >= 1)
> (FP is >= 0)
>
> but I get the following error
>
> Error: no valid set of coefficients has been found:please supply starting 
> values
> In addition: Warning message:
> NaNs produced in: log(x)
And did you follow the advice in the error message?
> in contrast if I fit a model without intercept
>
> fit <- glm(FP ~ rand - 1, data = tab, family = poisson(link =
"identity"),
> subset = rand >= 1)
>
> everything goes fine.
> Now my guess is that the points "naturally" have a negative
intercept so the
> error is produced because I'm using the poisson distribution for the y
and
> negative values are of course not admitted. Am I right?
We don't have your data, but it is plausible.
> Also if this is the cause, shouldn't the function always try to do the
best
> fit given the parameters? I mean shouldn't it fit a model with
intercept 0
> anyway and report it as a bad fit?
Well, I believe functions should do what they say on the box (and the help 
page), and not what some user hopes they might do by mind-reading.

You do have a suitable set of starting values from the second fit, so why 
not just follow the rather explicit advice?

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595