On 17/05/2012 20:35, Sophie Baillargeon wrote:
Hi,
When I run the following code :
Y<- c(rep(0,35),1,2,0,6,8,16,43)
cst<- log(choose(42, 42:1))
beta<- 42:1
tau<- (beta^2)/2
fit<- glm(formula = Y ~ offset(cst) + beta + tau, family = poisson)
fit
fit$converged
glm prints a warning saying that the algorithm did not converge.
Actually, no, it did not. The actual message is
> Warning: glm.fit: algorithm did not converge
Had you shown us that rather than misquote it, all would have been clearer.
However, fit$converged takes the value TRUE.
I don't understand why fit$converged is not always FALSE when the
warning "algorithm did not converge" is produced. Could someone help me
understand why I get this result?
Because there are two fits involved, and one of them does not converge.
Because you have an intercept and offset in your model, the null fit
also needs to be done, and that fit did not converge.
So the main results from your fit are reliable, but the null.deviance
component is not. Increase maxit to allow the null fit to converge.
Thanks a lot,
Sophie
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