glm() fitting using the brglmFit()
method.Typically only used internally by brglmFit(), but may be used to
construct a control argument.
mdyplControl(alpha = NULL, epsilon = 1e-08, maxit = 25, trace = FALSE)mdypl_control(alpha = NULL, epsilon = 1e-08, maxit = 25, trace = FALSE)
A list with components named as the arguments.
the shrinkage parameter (in [0, 1]) in the
Diaconis-Ylvisaker prior penalty. Default is NULL, which
results in alpha = n / (n + p), where n is the sum of the
binomial totals and p is the number of model
parameters. Setting alpha = 1 corresponds to using maximum
likelihood, i.e. no penalization. See Details.
positive convergence tolerance epsilon. Default is
1e-08.
integer giving the maximal number of iterations
allowed. Default is 25.
logical indicating if output should be produced for
each iteration. Default is FALSE.
Ioannis Kosmidis [aut, cre] ioannis.kosmidis@warwick.ac.uk
Internally, mdyplFit() uses stats::glm.fit() to fit a logistic
regression model on responses alpha * y + (1 - alpha) / 2, where
y are the original binomial responses scaled by the binomial
totals. epsilon, maxit and trace control the
stats::glm.fit() call; see stats::glm.control().
mdyplFit(), glm.control()