distrMod (version 2.1.1)

Confint-class: Confint-class

Description

Return value S4 classes for method ``confint''.

Objects from the Class

Objects could in principle be created by calls of the form new("Confint", ...). The preferred form is to have them created via a call to confint.

Details for methods <code>show</code>, <code>print</code>

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 type of the confidence interval(s) and its/their values. When show.detail is matched to "medium", you will in addition see the type of the estimator(s) for which it is produced, the corresponding call of the estimater, its sample size, and, if present, the value of the corresponding nuisance parameter. Finally, when show.detail is matched to "maximal", additionally you will be shown the fixed part of the parameter (if present) and the transformation of the estimator (if non-trivial, i.e. the identity) in form of its function code respectively of its derivative matrix.

concept

confidence interval

See Also

Estimator, confint, Estimate-class, trafo-methods

Examples

## some transformation
mtrafo <- function(x){
     nms0 <- c("scale","shape")
     nms <- c("shape","rate")
     fval0 <- c(x[2], 1/x[1])
     names(fval0) <- nms
     mat0 <- matrix( c(0, -1/x[1]^2, 1, 0), nrow = 2, ncol = 2,
                     dimnames = list(nms,nms0))                          
     list(fval = fval0, mat = mat0)}

x <- rgamma(50, scale = 0.5, shape = 3)

## parametric family of probability measures
G <- GammaFamily(scale = 1, shape = 2, trafo = mtrafo)
## MLE
res <- MLEstimator(x = x, ParamFamily = G)
ci <- confint(res)
print(ci, digits = 4, show.details="maximal")
print(ci, digits = 4, show.details="medium")
print(ci, digits = 4, show.details="minimal")