RobLox (version 1.2.0)

RobLox-package: Optimally robust influence curves and estimators for location and scale

Description

Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale.

Package versions

Note: The first two numbers of package versions do not necessarily reflect package-individual development, but rather are chosen for the RobAStXXX family as a whole in order to ease updating "depends" information.

Details

Package:	RobLox
Version:	1.2.0
Date:	2019-04-02
Depends:	R(>= 3.4), stats, distrMod(>= 2.8.0), RobAStBase(>= 1.2.0)
Imports:	methods, lattice, RColorBrewer, Biobase, RandVar(>= 1.2.0), distr(>= 2.8.0)
Suggests:	MASS
ByteCompile:	yes
License:	LGPL-3
URL:	http://robast.r-forge.r-project.org/
VCS/SVNRevision:	1214

References

M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Dissertation. University of Bayreuth. Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer. Rieder, H., Kohl, M. and Ruckdeschel, P. (2008). The Costs of not Knowing the Radius. Statistical Methods and Applications 17(1) 13-40. Extended version: http://r-kurs.de/RRlong.pdf

M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Application, 19(3):333-354.

See Also

RobAStBase-package

Examples

# NOT RUN {
library(RobLox)
ind <- rbinom(100, size=1, prob=0.05) 
x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9)
roblox(x)
res <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE)
estimate(res)
confint(res)
confint(res, method = symmetricBias())
pIC(res)
## don't run to reduce check time on CRAN
# }
# NOT RUN {
checkIC(pIC(res))
Risks(pIC(res))
Infos(pIC(res))
plot(pIC(res))
infoPlot(pIC(res))
# }
# NOT RUN {
## row-wise application
ind <- rbinom(200, size=1, prob=0.05) 
X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2)
rowRoblox(X)
# }