Comments (2)
Thanks, for reporting. Yes indeed in http://doi.org/10.1093/biomet/asaa052 we prove that penalized logistic regression produces always finite estimates even if the maximum likelihood estimates have infinite components.
The convergence problems happen rarely but do happen, and are due to the generality of the quasi-Fisher scoring iteration that brglmFit
implements (trying to cover for all possible glms and model matrices!). In those cases, some tweaking of the iteration (see ?brglm_control
) or supplying better starting values does the trick.
For example, "slowing down" the iteration a bit (using the slowit
argument; see ?brglm_control
) does the job for your data.
glm(dv ~ year, data = data, family = binomial(), method = "brglmFit", slowit = 0.5)
from brglm2.
Works, thanks a lot for the help
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Related Issues (20)
- Profiling methods HOT 2
- Multi-type fitter and diagnostics
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- AIC appears to be -2*loglik without parameter penalty HOT 1
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- Support for Ordered Logistic Regression (cumulative family object) HOT 3
- Step{stat} function does not pick the the model with the lowest AIC HOT 4
- Support for quasi... families? HOT 1
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