Bias Reduction in Binomial-Response Generalized Linear Models
Auxiliary for Controlling BRGLM Fitting
Bias reduction in Binomial-response GLMs
Computes confidence intervals of parameters for bias-reduced estimatio...
Calculates the Leverages for a GLM through a C Routine
Auxiliary for Controlling BRGLM Fitting
Additive Modifications to the Binomial Responses and Totals for Use wi...
Plot methods for 'profile.brglm' objects
Calculate profiles for objects of class 'brglm'.
Objectives to be profiled
Separation Identification.
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.