mceCalc and mleCalcmeRes(x, estimate, criterion.value, param, crit.fct, method = "explicit solution", crit.name = "Maximum Likelihood", Infos, warns = "", startPar = NULL)
get.criterion.fct(theta, Data, ParamFam, criterion.ff, fun, ...)
"samplesize"(object)ParamFamParameter; the parameter valueminuslogl when an object of
class MCEstimate is coerced to class mle
(from package stats4); to this end function get.criterion.fct
(also see details below) is helpful (at least if the dimension of the
estimator is larger than 1).ParamFamily;
the parametric family at which to evaluate the MCEStartPar --- starting parameter used.optim / optimizemceCalc and mleCalc by the internal helper function
.process.meCalcRes.get.criterion.fct produces a function criterion.fct
to fill slot minuslogl when an object of class MCEstimate
is coerced to class mle (from package stats4);
this way we may use profiling methods introduced there also for objects
of our classes. More specifically, we produce a function where all
coordinates/components of theta appear as separate named
arguments, which then calls fun with these separate arguments
again stacked to one (named) vector argument;samplesize determines the samplesize of argument object,i.e.;
if object has an attribute dim, it returns dim(object)[2],
else length(object).