All parameters in betareg
are estimated by maximum likelihood
using optim
with control options set in betareg.control
.
Most arguments are passed on directly to optim
, and start
controls
how optim
is called.
After the optim
maximization, an additional (quasi) Fisher scoring
can be perfomed to further enhance the result or to perform additional bias reduction.
If fsmaxit
is greater than zero, this additional optimization is
performed and it converges if the threshold fstol
is attained
for the cross-product of the step size.
Starting values can be supplied via start
or estimated by
lm.wfit
, using the link-transformed response.
Covariances are in general derived analytically. Only if type = "ML"
and
hessian = TRUE
, they are determined numerically using the Hessian matrix
returned by optim
. In the latter case no Fisher scoring iterations are
performed.
The main parameters of interest are the coefficients in the linear predictor of the
model and the additional precision parameter phi which can either
be treated as a full model parameter (default) or as a nuisance parameter. In the latter case
the estimation does not change, only the reported information in output from print
,
summary
, or coef
(among others) will be different. See also examples.