Fitting Additive Binomial Regression Models
Workhorse function for
binomial response. May be a single column of 0/1 or two columns, giving the number of successes and failures.
non-negative design matrix. Must have an intercept column.
starting values for the parameters in the linear predictor.
list of parameters for controlling the
fitting process, passed to
a list of all parameterisations for this
model, obtained from
An additive binomial fit can be converted into an additive
Poisson fit via the multinomial–Poisson transformation
(Baker, 1994). This function transforms the data as
described by Donoghoe and Marschner (2014) and passes it to
with a Poisson family to get the maximum likelihood
estimate. The coefficients (and other values) from the
Poisson model are transformed back to relate to the
additive binomial model.
This is a workhorse function for
binomial family is specified. It would not usually
be called directly.
A list of (most of) the components needed for an object of
Mark W. Donoghoe Mark.Donoghoe@mq.edu.au
Baker, S. G. (1994). The multinomial–Poisson transformation. The Statistician 43(4): 495–504.
Donoghoe, M. W. and I. C. Marschner (2014). Stable computational methods for additive binomial models with application to adjusted risk differences. Computational Statistics and Data Analysis 80: 184–196.
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