| 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())