MCEstimate-class {distrMod} | R Documentation |
Class of minimum criterion estimates.
Objects can be created by calls of the form new("MCEstimate", ...)
.
More frequently they are created via the generating functions
MCEstimator
, MDEstimator
or MLEstimator
.
name
:"character"
:
name of the estimator. estimate
:"ANY"
:
estimate.estimate.call
:"call"
:
call by which estimate was produced.criterion
:"numeric"
:
minimum value of the considered criterion.criterion.fct
:"function"
:
the considered criterion function; used for compatibility with class
"mle"
from package stats4; should be a function
returning the criterion; i.e. a numeric of length 1 and should have
as arguments all named components of argument
untransformed.estimate
method
:"character"
:
the method by which the estimate was calculated, i.e.;
"optim"
, "optimize"
, or "explicit calculation"
;
used for compatibility with class "mle"
from package
stats4, could be any character value.Infos
:"matrix"
with two columns named method
and message
:
additional informations. asvar
:"OptionalMatrix"
which may contain the asymptotic (co)variance of the estimator. samplesize
:"numeric"
—
the samplesize at which the estimate was evaluated. nuis.idx
:"OptionalNumeric"
:
indices of estimate
belonging to the nuisance partuntransformed.estimate
:"ANY"
:
untransformed estimate.untransformed.asvar
:"OptionalNumericOrMatrix"
which may contain the asymptotic (co)variance of the untransformed
estimator.
Class "Estimate"
, directly.
signature(object = "MCEstimate")
:
accessor function for slot criterion
. signature(object = "MCEstimate")
:
replacement function for slot criterion
. signature(object = "MCEstimate")
:
accessor function for slot criterion.fct
. signature(object = "Estimate")
signature(from = "MCEstimate", to = "mle")
:
create a "mle"
object from a "MCEstimate"
objectsignature(fitted = "MCEstimate")
:
coerces fitted
to class "mle"
and then calls
the corresponding profile
-method
from package stats4; for details we confer to the corresponding
man page.
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel Peter.Ruckdeschel@itwm.fraunhofer.de
Estimate-class
, MCEstimator
,
MDEstimator
, MLEstimator
## (empirical) Data x <- rgamma(50, scale = 0.5, shape = 3) ## parametric family of probability measures G <- GammaFamily(scale = 1, shape = 2) MDEstimator(x, G) (m <- MLEstimator(x, G)) m.mle <- as(m,"mle") par(mfrow=c(1,2)) plot(profile(m))