ROptEst-package {ROptEst} | R Documentation |
Optimally robust estimation in general smoothly parameterized models using S4 classes and methods.
Package: | ROptEst |
Version: | 0.6.2 |
Date: | 2008-11-27 |
Depends: | R(>= 2.7.0), methods, distr(>= 2.0), distrEx(>= 2.0),distrMod(>= 2.0), RandVar(>= 0.6.4), RobAStBase |
LazyLoad: | yes |
License: | LGPL-3 |
URL: | http://robast.r-forge.r-project.org/ |
Peter Ruckdeschel Peter.Ruckdeschel@itwm.fraunhofer.de,
Matthias Kohl Matthias.Kohl@stamats.de
Maintainer: Matthias Kohl matthias.kohl@stamats.de
M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Dissertation. University of Bayreuth.
distr-package
, distrEx-package
,
distrMod-package
, RandVar-package
,
RobAStBase-package
library(ROptEst) ## Example: Rutherford-Geiger (1910); cf. Feller~(1968), Section VI.7 (a) x <- c(rep(0, 57), rep(1, 203), rep(2, 383), rep(3, 525), rep(4, 532), rep(5, 408), rep(6, 273), rep(7, 139), rep(8, 45), rep(9, 27), rep(10, 10), rep(11, 4), rep(12, 0), rep(13, 1), rep(14, 1)) ## ML-estimate from package distrMod MLest <- MLEstimator(x, PoisFamily()) MLest ## confidence interval based on CLT confint(MLest) ## compute optimally (w.r.t to MSE) robust estimator (unknown contamination) robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3) estimate(robest) ## check influence curve checkIC(pIC(robest)) ## plot influence curve plot(pIC(robest)) ## confidence interval based on LAN - neglecting bias confint(robest) ## confidence interval based on LAN - including bias confint(robest, method = symmetricBias())