Estimate-class: Estimate-class.

Description Objects from the Class Slots Methods Details for methods 'show', 'print' Note Author(s) See Also Examples


Class of estimates.

Objects from the Class

Objects can be created by calls of the form new("Estimate", ...). More frequently they are created via the generating function Estimator.



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


Object of class "ANY": estimate.

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


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


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


object of class "numeric" — the samplesize (only complete cases are counted) at which the estimate was evaluated.


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


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


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


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


Object of class "ANY": untransformed estimate.


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



signature(object = "Estimate"): accessor function for slot name.


signature(object = "Estimate"): replacement function for slot name.


signature(object = "Estimate"): accessor function for slot estimate.


signature(object = "Estimate"): accessor function for slot untransformed.estimate.

signature(object = "Estimate"): accessor function for slot


signature(object = "Estimate"): (with additional argument onlycompletecases defaulting to TRUE returns the sample size; in case there are any incomplete cases and argument onlycompletecases is FALSE, the number of these is added to slot samplesize.


signature(object = "Estimate"): accessor function for slot completecases.


signature(object = "Estimate"): accessor function for slot asvar.


signature(object = "Estimate"): replacement function for slot asvar.


signature(object = "Estimate"): accessor function for slot untransformed.asvar.


signature(object = "Estimate"): accessor function for nuisance part of slot estimate.


signature(object = "Estimate"): accessor function for main part of slot estimate.


signature(object = "Estimate"): accessor function for slot fixed.


signature(object = "Estimate"): accessor function for slot Infos.


signature(object = "Estimate"): replacement function for slot Infos.


signature(object = "Estimate"): function to add an information to slot Infos.


signature(object = "Estimate")


signature(object = "Estimate"): just as show, but with additional arguments digits.

Details for methods 'show', 'print'

Detailedness of output by methods show, print is controlled by the global option show.details to be set by distrModoptions.

As method show is used when inspecting an object by typing the object's name into the console, show comes without extra arguments and hence detailedness must be controlled by global options.

Method print may be called with a (partially matched) argument show.details, and then the global option is temporarily set to this value.

More specifically, when show.detail is matched to "minimal" you will be shown only the name/type of the estimator, the value of its main part, and, if present, the corresponding standard errors, as well as, also if present, the value of the nuisance part. When show.detail is matched to "medium", you will in addition see the class of the estimator, its call and its sample-size and, if present, the fixed part of the parameter and the asymptotic covariance matrix. Also the information gathered in the Infos slot is shown. Finally, when show.detail is matched to "maximal", and if, in addition, you estimate non-trivial (i.e. not the identity) transformation of the parameter of the parametric family, you will also be shown this transformation in form of its function and its derivative matrix at the estimated parameter value, as well as the estimator (with standard errors, if present) and (again, if present) the corresponding asymptotic covariance of the untransformed, total (i.e. main and nuisance part) parameter.

trafo realizes partial influence curves; i.e.; we are only interested is some possibly lower dimensional smooth (not necessarily linear or even coordinate-wise) aspect/transformation tau of the parameter theta.

To be coherent with the corresponding nuisance implementation, we make the following convention:

The full parameter theta is split up coordinate-wise in a main parameter theta' and a nuisance parameter theta'' (which is unknown, too, hence has to be estimated, but only is of secondary interest) and a fixed, known part theta'''.

Without loss of generality, we restrict ourselves to the case that transformation tau only acts on the main parameter theta' — if we want to transform the whole parameter, we only have to assume that both nuisance parameter theta'' and fixed, known part of the parameter theta''' have length 0.

To the implementation:

Slot trafo can either contain a (constant) matrix D_theta or a function

tau: Theta' -> TTheta, theta |-> tau(theta)

mapping main parameter theta' to some range TTheta.

If slot value trafo is a function, besides tau(theta), it will also return the corresponding derivative matrix (d/d theta) (tau(theta)). More specifically, the return value of this function theta is a list with entries fval, the function value tau(theta), and mat, the derivative matrix.

In case trafo is a matrix D, we interpret it as such a derivative matrix (d/d theta) (tau(theta)), and, correspondingly, tau(theta) as the linear mapping tau(theta)=D * theta.


The pretty-printing code for methods show and print has been borrowed from print.fitdistr in package MASS by B.D. Ripley.


Matthias Kohl,
Peter Ruckdeschel

See Also



x <- rnorm(100)
Estimator(x, estimator = mean, name = "mean")

x1 <- x; x1[sample(1:100,10)] <- NA
myEst1 <- Estimator(x1, estimator = mean, name = "mean")
samplesize(myEst1, onlycomplete = FALSE)

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