new("Estimate", ...).
More frequently they are created via the generating function
Estimator.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
$\tau(\theta)=D\,\theta$.Estimatorx <- 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)Run the code above in your browser using DataLab