helper functions to produce consistent lists to be digested
in functions mceCalc
and mleCalc
meRes(x, estimate, criterion.value, param, crit.fct, method = "explicit solution",
crit.name = "Maximum Likelihood", Infos, warns = "", startPar = NULL,
optReturn = NULL)
get.criterion.fct(theta, Data, ParamFam, criterion.ff, fun, ...)
# S4 method for numeric
samplesize(object)
a list of prescribed structure to be digested in functions
mceCalc
and mleCalc
by the internal helper function
.process.meCalcRes
.
a function; see details below;
numeric
numeric; the data at which to evaluate the estimate
numeric; the estimate
numeric; the value of the criterion
object of class ParamFamParameter
; the parameter value
a function to fill slot minuslogl
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).
character; describes how the estimate was obtained
character; name of the criterion
optional matrix of characters in two columns; information to be attached to the estimate
collected warnings in optimization
numeric; the sample size at which the estimator was evaluated
the parameter value as named numeric vector
numeric; the data at which to evaluate the MCE
an object of class ParamFamily
;
the parametric family at which to evaluate the MCE
the criterion function used in the MCE
wrapper to the criterion function used in the MCE (with certain checking whether parameter value is permitted and possibly penalizing if not; see code to , for example.)
value of argument StartPar
--- starting parameter used.
object of class "ANY"
the return value of the optimizer (or NULL
if, e.g.,
closed form solutions are used).
further arguments to be passed
to optim
/ optimize
numeric; the data at which to evaluate the estimate
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
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)
.