The wdm
function calls an estimation routine, to estimate the
model parameters.
If all but one parameters are fixed, a "Brent
(optim)"
type algorithm is used. For the estimation of more than one
parameter, first a "BFGS (optim)"
type algorithm is tried, if
unsuccessful, a "Newton type (nlm)"
algorithm is tried, if again
unsuccessful, a "Nelder-Mead (optim)"
algorithm is used.
In case all parameters are set to fixed values, no estimation routine is
called, but a wdm
object will still be created.
The returned wdm
object is basically a list containing the
parameter estimates in $coefficients
. $hessian
contains the
numerically differentiated Hessian matrix (if available, else NULL).
$data
contains the data passed to the wdm
function call.
$loglik
contains the log-likelihood value for the wdm
object and its parameter estimates. $estpar
contains a vector,
that is TRUE
if the respective parameter was estimated and
FALSE
if the respective parameter was set to a fixed value.
Additional information is given in other list objects.
The standard R functions coef
, vcov
, confint
,
summary
can be used with wdm
objects.