| 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.estimatemethod:"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))