[R] starting values in glm(..., family = binomial(link = log))

[Ben Bolker]

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))