mceCalc-methods: Methods for functions mceCalc and mleCalc in Package `distrMod'

Description

Methods for functions mceCalc and mleCalc in package distrMod;

Usage

mceCalc(x, PFam, ...)
mleCalc(x, PFam, ...)
# S4 method for numeric,ParamFamily
mceCalc(x, PFam, criterion, 
                   startPar = NULL, penalty = 1e20, crit.name,
                   Infos = NULL, validity.check = TRUE,
                   withthetaPar = FALSE,...)
# S4 method for numeric,ParamFamily
mleCalc(x, PFam, startPar = NULL, 
                   penalty = 1e20, dropZeroDensity = TRUE, Infos = NULL,
                    validity.check = TRUE, ...)
# S4 method for numeric,BinomFamily
mleCalc(x, PFam, ...)
# S4 method for numeric,PoisFamily
mleCalc(x, PFam, ...)
# S4 method for numeric,NormLocationFamily
mleCalc(x, PFam, ...)
# S4 method for numeric,NormScaleFamily
mleCalc(x, PFam, ...)
# S4 method for numeric,NormLocationScaleFamily
mleCalc(x, PFam, ...)

Value

a list with components

estimate: --- the estimate as a named vector of numeric
criterion: --- the criterion value (i.e.; a numeric of length 1); e.g. the neg. log likelihood
est.name: --- the name of the estimator
param: --- estimate coerced to class ParamFamParameter
crit.fct: --- a function with the named components of theta as arguments returning the criterion value; used for profiling / coercing to class mle
method: --- a character reporting how the estimate was obtained, i.e., by optim, by optimize or by explicit calculations
crit.name: character; the name of the criterion; may be ""
Infos: matrix; info slot to be filled in object of class MCEstimate; may be NULL
samplesize: numeric; sample size of x

Arguments

x: numeric; data at which to evaluate the estimator
PFam: an object of class ParamFamily; the parametric family at which to evaluate the estimator
criterion: a function measuring the ``goodness of fit''
startPar: in case optim is used: a starting value for the parameter fit; in case optimize is used: a vector containing a search interval for the (one-dim) parameter
penalty: numeric; penalizes non-permitted parameter values
crit.name: character; the name of the criterion; may be missing
withthetaPar: logical; shall Parameter theta be transmitted?
Infos: matrix; info slot to be filled in object of class MCEstimate; may be missing
validity.check: logical: shall return parameter value be checked for validity?
dropZeroDensity: logical of length 1; shall observations with density zero be dropped? Optimizers like optim require finite values, so get problems when negative loglikelihood is evaluated.
...: additional argument(s) for optim / optimize

Details

mceCalc is used internally by function MCEstimator to allow for method dispatch according to argument PFam; similarly, and for the same purpose mleCalc is used internally by function MLEstimator. This way we / or any other developper can write particular methods for special cases where we may avoid using numerical optimization without interfering with existing code. For programming one's own mleCalc / mceCalc methods, there is the helper function meRes to produce consistent return values.