Class of estimates.
Objects can be created by calls of the form new("Estimate", ...)
.
More frequently they are created via the generating function
Estimator
.
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.
Infos
object of class "matrix"
with two columns named method
and message
:
additional informations.
asvar
object of class "OptionalNumericOrMatrix"
which may contain the asymptotic (co)variance of the estimator.
samplesize
object of class "numeric"
---
the samplesize (only complete cases are counted)
at which the estimate was evaluated.
completecases
object of class "logical"
---
complete cases 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.
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 estimate.call
.
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
.
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
$$\tau\colon \Theta' \to \tilde \Theta,\qquad \theta \mapsto \tau(\theta)$$
mapping main parameter
\(\theta'\) to some range \(\tilde \Theta\).
If slot value trafo
is a function, besides \(\tau(\theta)\),
it will also return the corresponding derivative matrix
\(\frac{\partial}{\partial \theta}\tau(\theta)\).
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 \(\frac{\partial}{\partial \theta}\tau(\theta)\),
and, correspondingly, \(\tau(\theta)\) as the linear mapping
\(\tau(\theta)=D\,\theta\).
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Estimator
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)
samplesize(myEst1, onlycomplete = FALSE)
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