MCEstimate-class: MCEstimate-class.

In distrMod: Object Oriented Implementation of Probability Models

MCEstimate-class.

Description

Class of minimum criterion estimates.

Objects from the Class

Objects can be created by calls of the form new("MCEstimate", ...). More frequently they are created via the generating functions MCEstimator, MDEstimator or MLEstimator. More specifically, MDEstimator, CvMMDEstimator, and MLEstimator return objects of classes MDEstimate, CvMMDEstimate, and MLEstimate respectively, which each are immediate subclasses of MCEstimate (without further slots, for internal use in method dispatch).

Slots

name

Object of class "character": name of the estimator.

estimate

Object of class "ANY": estimate.

estimate.call

Object of class "call": call by which estimate was produced.

criterion

Object of class "numeric": minimum value of the considered criterion.

criterion.fct

Object of class "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

Object of class "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

object of class "matrix" with two columns named method and message: additional informations.

optimwarn

object of class "character" warnings issued during optimization.

optimReturn

object of class "ANY" the return value of the optimizer (or NULL if, e.g., closed form solutions are used).

startPar

— object of class "ANY"; filled either with NULL (no starting value used) or with "numeric" — the value of the starting parameter.

asvar

object of class "OptionalMatrix" which may contain the asymptotic (co)variance of the estimator.

samplesize

object of class "numeric" — the samplesize at which the estimate was evaluated.

nuis.idx

object of class "OptionalNumeric": indices of estimate belonging to the nuisance part

fixed

object of class "OptionalNumeric": the fixed and known part of the parameter.

trafo

object of class "list": a list with components fct and mat (see below).

untransformed.estimate

Object of class "ANY": untransformed estimate.

untransformed.asvar

object of class "OptionalNumericOrMatrix" which may contain the asymptotic (co)variance of the untransformed estimator.

completecases

object of class "logical" — complete cases at which the estimate was evaluated.

startPar

object of class "ANY"; usually filled with argument startPar of generating function MCEstimator, MLEstimator, MDEstimator.

Extends

Class "Estimate", directly.

Methods

criterion

signature(object = "MCEstimate"): accessor function for slot criterion.

criterion<-

signature(object = "MCEstimate"): replacement function for slot criterion.

optimwarn

signature(object = "MCEstimate"): accessor function for slot optimwarn.

optimReturn

signature(object = "MCEstimate"): accessor function for slot optimReturn.

startPar

signature(object = "MCEstimate"): accessor function for slot startPar.

criterion.fct

signature(object = "MCEstimate"): accessor function for slot criterion.fct.

show

signature(object = "Estimate")

coerce

signature(from = "MCEstimate", to = "mle"): create a "mle" object from a "MCEstimate" object

profile

signature(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.

Author(s)

Matthias Kohl [e-mail],
Peter Ruckdeschel [e-mail]

See Also

Estimate-class, MCEstimator, MDEstimator, MLEstimator

Examples

## (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))
profileM <- profile(m)
## plot-profile throws an error

distrMod documentation built on Nov. 16, 2022, 9:07 a.m.