Fischer, Felix <Felix.Fischer <at> charite.de> writes:
>
> Dear R-helpers,
> i have a problem with a glm-model. I am trying to fit models with
> the log as link function instead of the logit. However, in some
> cases glm fails to estimate those models and suggests to give start
> values. However, when I set start = coef(logistic_model) within the
> function call, glm still says it cannot find starting values? This
> seems to be more of a problem, when I include a continous predictor
> in the model (age instead of group). find below a minimal example.
[Sorry for snipping context: gmane doesn't like it]
Group_logit_model = glm(data = x, Arthrose ~ Gruppe,
family=binomial(link = logit))
Group_log_model = glm(data = x, Arthrose ~ Gruppe,
family=binomial(link = log))
Age_logit_model = glm(data = x, Arthrose ~ Alter,
family=binomial(link = logit))
Age_log_model = glm(data = x, Arthrose ~ Alter,
family=binomial(link = log),
start=c(coef(Group_log_model)[1],0))
Using the intercept from the group_log model combined with 0
for the log-slope appears to work. It makes more sense to use
this than to use the results from a logit fit (as you tried),
because those parameters would be on a different scale.
Another possibility for the starting
intercept value would be the coefficient of a null model
with a log-link:
Null_log_model = glm(data = x, Arthrose ~ 1,
family=binomial(link = log))
______________________________________________
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
and provide commented, minimal, self-contained, reproducible code.
