[Robast-commits] r388 - in branches/robast-0.7/pkg/ROptEst: . tests tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Oct 16 12:09:12 CEST 2009
Author: stamats
Date: 2009-10-16 12:09:11 +0200 (Fri, 16 Oct 2009)
New Revision: 388
Added:
branches/robast-0.7/pkg/ROptEst/tests/
branches/robast-0.7/pkg/ROptEst/tests/Examples/
branches/robast-0.7/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
Log:
forgot to add tests folder ... argh
Added: branches/robast-0.7/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
===================================================================
--- branches/robast-0.7/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save (rev 0)
+++ branches/robast-0.7/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save 2009-10-16 10:09:11 UTC (rev 388)
@@ -0,0 +1,1958 @@
+
+R version 2.10.0 beta (2009-10-15 r50107)
+Copyright (C) 2009 The R Foundation for Statistical Computing
+ISBN 3-900051-07-0
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+ Natural language support but running in an English locale
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> ### * <HEADER>
+> ###
+> attach(NULL, name = "CheckExEnv")
+> assign("nameEx",
++ local({
++ s <- "__{must remake R-ex/*.R}__"
++ function(new) {
++ if(!missing(new)) s <<- new else s
++ }
++ }),
++ pos = "CheckExEnv")
+> ## Add some hooks to label plot pages for base and grid graphics
+> assign("base_plot_hook",
++ function() {
++ pp <- par(c("mfg","mfcol","oma","mar"))
++ if(all(pp$mfg[1:2] == c(1, pp$mfcol[2]))) {
++ outer <- (oma4 <- pp$oma[4]) > 0; mar4 <- pp$mar[4]
++ mtext(sprintf("help(\"%s\")", nameEx()), side = 4,
++ line = if(outer)max(1, oma4 - 1) else min(1, mar4 - 1),
++ outer = outer, adj = 1, cex = .8, col = "orchid", las=3)
++ }
++ },
++ pos = "CheckExEnv")
+> assign("grid_plot_hook",
++ function() {
++ grid::pushViewport(grid::viewport(width=grid::unit(1, "npc") -
++ grid::unit(1, "lines"), x=0, just="left"))
++ grid::grid.text(sprintf("help(\"%s\")", nameEx()),
++ x=grid::unit(1, "npc") + grid::unit(0.5, "lines"),
++ y=grid::unit(0.8, "npc"), rot=90,
++ gp=grid::gpar(col="orchid"))
++ },
++ pos = "CheckExEnv")
+> setHook("plot.new", get("base_plot_hook", pos = "CheckExEnv"))
+> setHook("persp", get("base_plot_hook", pos = "CheckExEnv"))
+> setHook("grid.newpage", get("grid_plot_hook", pos = "CheckExEnv"))
+> assign("cleanEx",
++ function(env = .GlobalEnv) {
++ rm(list = ls(envir = env, all.names = TRUE), envir = env)
++ RNGkind("default", "default")
++ set.seed(1)
++ options(warn = 1)
++ .CheckExEnv <- as.environment("CheckExEnv")
++ delayedAssign("T", stop("T used instead of TRUE"),
++ assign.env = .CheckExEnv)
++ delayedAssign("F", stop("F used instead of FALSE"),
++ assign.env = .CheckExEnv)
++ sch <- search()
++ newitems <- sch[! sch %in% .oldSearch]
++ for(item in rev(newitems))
++ eval(substitute(detach(item), list(item=item)))
++ missitems <- .oldSearch[! .oldSearch %in% sch]
++ if(length(missitems))
++ warning("items ", paste(missitems, collapse=", "),
++ " have been removed from the search path")
++ },
++ pos = "CheckExEnv")
+> assign("ptime", proc.time(), pos = "CheckExEnv")
+> ## at least one package changes these via ps.options(), so do this
+> ## before loading the package.
+> ## Use postscript as incomplete files may be viewable, unlike PDF.
+> ## Choose a size that is close to on-screen devices, fix paper
+> grDevices::ps.options(width = 7, height = 7, paper = "a4", reset = TRUE)
+> grDevices::postscript("ROptEst-Ex.ps")
+>
+> assign("par.postscript", graphics::par(no.readonly = TRUE), pos = "CheckExEnv")
+> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
+> options(warn = 1)
+> library('ROptEst')
+Loading required package: distr
+Loading required package: startupmsg
+:startupmsg> Utilities for start-up messages (version 0.7)
+:startupmsg>
+:startupmsg> For more information see ?"startupmsg",
+:startupmsg> NEWS("startupmsg")
+
+Loading required package: sfsmisc
+Loading required package: SweaveListingUtils
+:SweaveListingUtils> Utilities for Sweave together with
+:SweaveListingUtils> TeX listings package (version 0.4)
+:SweaveListingUtils>
+:SweaveListingUtils> Some functions from package 'base'
+:SweaveListingUtils> are intentionally masked ---see
+:SweaveListingUtils> SweaveListingMASK().
+:SweaveListingUtils>
+:SweaveListingUtils> Note that global options are
+:SweaveListingUtils> controlled by
+:SweaveListingUtils> SweaveListingoptions() ---c.f.
+:SweaveListingUtils> ?"SweaveListingoptions".
+:SweaveListingUtils>
+:SweaveListingUtils> For more information see
+:SweaveListingUtils> ?"SweaveListingUtils",
+:SweaveListingUtils> NEWS("SweaveListingUtils")
+:SweaveListingUtils> There is a vignette to this
+:SweaveListingUtils> package; try
+:SweaveListingUtils> vignette("ExampleSweaveListingUtils").
+
+
+Attaching package: 'SweaveListingUtils'
+
+
+ The following object(s) are masked from package:base :
+
+ library,
+ require
+
+:distr> Object orientated implementation of distributions (version
+:distr> 2.2)
+:distr>
+:distr> Attention: Arithmetics on distribution objects are
+:distr> understood as operations on corresponding random variables
+:distr> (r.v.s); see distrARITH().
+:distr>
+:distr> Some functions from package 'stats' are intentionally masked
+:distr> ---see distrMASK().
+:distr>
+:distr> Note that global options are controlled by distroptions()
+:distr> ---c.f. ?"distroptions".
+:distr>
+:distr> For more information see ?"distr", NEWS("distr"), as well as
+:distr> http://distr.r-forge.r-project.org/
+:distr> Package "distrDoc" provides a vignette to this package as
+:distr> well as to several extension packages; try
+:distr> vignette("distr").
+
+
+Attaching package: 'distr'
+
+
+ The following object(s) are masked from package:stats :
+
+ df,
+ qqplot,
+ sd
+
+Loading required package: distrEx
+Loading required package: evd
+Loading required package: actuar
+
+Attaching package: 'actuar'
+
+
+ The following object(s) are masked from package:grDevices :
+
+ cm
+
+:distrEx> Extensions of package distr (version 2.2)
+:distrEx>
+:distrEx> Note: Packages "e1071", "moments", "fBasics" should be
+:distrEx> attached /before/ package "distrEx". See distrExMASK().
+:distrEx>
+:distrEx> For more information see ?"distrEx", NEWS("distrEx"), as
+:distrEx> well as
+:distrEx> http://distr.r-forge.r-project.org/
+:distrEx> Package "distrDoc" provides a vignette to this package
+:distrEx> as well as to several related packages; try
+:distrEx> vignette("distr").
+
+
+Attaching package: 'distrEx'
+
+
+ The following object(s) are masked from package:stats :
+
+ IQR,
+ mad,
+ median,
+ var
+
+Loading required package: distrMod
+Loading required package: RandVar
+:RandVar> Implementation of random variables (version 0.7)
+:RandVar>
+:RandVar> For more information see ?"RandVar", NEWS("RandVar"), as
+:RandVar> well as
+:RandVar> http://robast.r-forge.r-project.org/
+:RandVar> This package also includes a vignette; try
+:RandVar> vignette("RandVar").
+
+Loading required package: MASS
+Loading required package: stats4
+:distrMod> Object orientated implementation of probability models
+:distrMod> (version 2.2)
+:distrMod>
+:distrMod> Some functions from pkg's 'base' and 'stats' are
+:distrMod> intentionally masked ---see distrModMASK().
+:distrMod>
+:distrMod> Note that global options are controlled by
+:distrMod> distrModoptions() ---c.f. ?"distrModoptions".
+:distrMod>
+:distrMod> For more information see ?"distrMod",
+:distrMod> NEWS("distrMod"), as well as
+:distrMod> http://distr.r-forge.r-project.org/
+:distrMod> Package "distrDoc" provides a vignette to this package
+:distrMod> as well as to several related packages; try
+:distrMod> vignette("distr").
+
+
+Attaching package: 'distrMod'
+
+
+ The following object(s) are masked from package:stats4 :
+
+ confint
+
+
+ The following object(s) are masked from package:stats :
+
+ confint
+
+Loading required package: RobAStBase
+:RobAStBase> Robust Asymptotic Statistics (version 0.7)
+:RobAStBase>
+:RobAStBase> Some functions from pkg's 'stats' and 'graphics'
+:RobAStBase> are intentionally masked ---see RobAStBaseMASK().
+:RobAStBase>
+:RobAStBase> Note that global options are controlled by
+:RobAStBase> RobAStBaseoptions() ---c.f. ?"RobAStBaseoptions".
+:RobAStBase>
+:RobAStBase> For more information see ?"RobAStBase",
+:RobAStBase> NEWS("RobAStBase"), as well as
+:RobAStBase> http://robast.r-forge.r-project.org/
+
+
+Attaching package: 'RobAStBase'
+
+
+ The following object(s) are masked from package:stats :
+
+ start
+
+
+ The following object(s) are masked from package:graphics :
+
+ clip
+
+>
+> assign(".oldSearch", search(), pos = 'CheckExEnv')
+> assign(".oldNS", loadedNamespaces(), pos = 'CheckExEnv')
+> cleanEx(); nameEx("0ROptEst-package")
+> ### * 0ROptEst-package
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: ROptEst-package
+> ### Title: Optimally robust estimation
+> ### Aliases: ROptEst-package ROptEst
+> ### Keywords: package
+>
+> ### ** Examples
+>
+> 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
+Evaluations of Maximum likelihood estimate:
+-------------------------------------------
+An object of class “Estimate”
+generated by call
+ MLEstimator(x = x, ParamFamily = PoisFamily())
+samplesize: 2608
+estimate:
+
+ 3.87154908
+ (0.03852908)
+asymptotic (co)variance (multiplied with samplesize):
+[1] 3.871549
+Criterion:
+negative log-likelihood
+ 5352.105
+> ## confidence interval based on CLT
+> confint(MLest)
+A[n] asymptotic (CLT-based) confidence interval:
+ 2.5 % 97.5 %
+[1,] 3.796033 3.947065
+Type of estimator: Maximum likelihood estimate
+samplesize: 2608
+Call by which estimate was produced:
+MLEstimator(x = x, ParamFamily = PoisFamily())
+>
+> ## compute optimally (w.r.t to MSE) robust estimator (unknown contamination)
+> robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
+> estimate(robest)
+ lambda
+3.908322
+> ## check influence curve
+> checkIC(pIC(robest))
+precision of centering: 2.6415e-16
+precision of Fisher consistency:
+ lambda
+lambda -1.968972e-06
+maximum deviation
+ 1.968972e-06
+> ## plot influence curve
+> plot(pIC(robest))
+> ## confidence interval based on LAN - neglecting bias
+> confint(robest)
+A[n] asymptotic (LAN-based) confidence interval:
+ 2.5 % 97.5 %
+lambda 3.826171 3.990474
+Type of estimator: 3-step estimate
+samplesize: 2608
+Call by which estimate was produced:
+roptest(x = x, L2Fam = PoisFamily(), eps.upper = 0.1, steps = 3)
+> ## confidence interval based on LAN - including bias
+> confint(robest, method = symmetricBias())
+A[n] asymptotic (LAN-based), uniform (bias-aware)
+ confidence interval:
+for symmetric Bias
+ 2.5 % 97.5 %
+lambda 3.759634 4.05701
+Type of estimator: 3-step estimate
+samplesize: 2608
+Call by which estimate was produced:
+roptest(x = x, L2Fam = PoisFamily(), eps.upper = 0.1, steps = 3)
+>
+>
+>
+> cleanEx(); nameEx("cniperCont")
+> ### * cniperCont
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: cniperCont
+> ### Title: Generic Functions for Computation and Plot of Cniper
+> ### Contamination and Cniper Points.
+> ### Aliases: cniperCont cniperCont-methods
+> ### cniperCont,IC,IC,L2ParamFamily,ContNeighborhood,asMSE-method
+> ### cniperPoint cniperPoint-methods
+> ### cniperPoint,L2ParamFamily,ContNeighborhood,asMSE-method
+> ### cniperPointPlot cniperPointPlot-methods
+> ### cniperPointPlot,L2ParamFamily,ContNeighborhood,asMSE-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> ## cniper contamination
+> P <- PoisFamily(lambda = 4)
+> RobP1 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 0.1))
+> IC1 <- optIC(model=RobP1, risk=asMSE())
+> RobP2 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 1))
+> IC2 <- optIC(model=RobP2, risk=asMSE())
+> cniperCont(IC1 = IC1, IC2 = IC2, L2Fam = P,
++ neighbor = ContNeighborhood(radius = 0.5),
++ risk = asMSE(),
++ lower = 0, upper = 8, n = 101)
+>
+> ## cniper point plot
+> cniperPointPlot(P, neighbor = ContNeighborhood(radius = 0.5),
++ risk = asMSE(), lower = 0, upper = 10)
+>
+> ## cniper point
+> cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
++ risk = asMSE(), lower = 0, upper = 4)
+cniper point
+ 0.7803439
+> cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
++ risk = asMSE(), lower = 4, upper = 8)
+cniper point
+ 7.219656
+>
+>
+>
+> cleanEx(); nameEx("getL1normL2deriv")
+> ### * getL1normL2deriv
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: getL1normL2deriv
+> ### Title: Calculation of L1 norm of L2derivative
+> ### Aliases: getL1normL2deriv getL1normL2deriv-methods
+> ### getL1normL2deriv,UnivariateDistribution-method
+> ### getL1normL2deriv,RealRandVariable-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> ##
+>
+>
+>
+> cleanEx(); nameEx("getL2normL2deriv")
+> ### * getL2normL2deriv
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: getL2normL2deriv
+> ### Title: Calculation of L2 norm of L2derivative
+> ### Aliases: getL2normL2deriv
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> ##
+>
+>
+>
+> cleanEx(); nameEx("leastFavorableRadius")
+> ### * leastFavorableRadius
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: leastFavorableRadius
+> ### Title: Generic Function for the Computation of Least Favorable Radii
+> ### Aliases: leastFavorableRadius leastFavorableRadius-methods
+> ### leastFavorableRadius,L2ParamFamily,UncondNeighborhood,asGRisk-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> N <- NormLocationFamily(mean=0, sd=1)
+> leastFavorableRadius(L2Fam=N, neighbor=ContNeighborhood(),
++ risk=asMSE(), rho=0.5)
+current radius: 0.3820278 inefficiency: 1.039514
+current radius: 0.6180722 inefficiency: 1.043963
+current radius: 0.7639556 inefficiency: 1.041503
+current radius: 0.6008356 inefficiency: 1.044073
+current radius: 0.5598913 inefficiency: 1.044123
+current radius: 0.4919535 inefficiency: 1.043417
+current radius: 0.5735221 inefficiency: 1.044142
+current radius: 0.5739285 inefficiency: 1.044142
+current radius: 0.5736396 inefficiency: 1.044142
+current radius: 0.5735989 inefficiency: 1.044142
+current radius: 0.5736803 inefficiency: 1.044142
+current radius: 0.5736396 inefficiency: 1.044142
+$rho
+[1] 0.5
+
+$leastFavorableRadius
+[1] 0.5736396
+
+$`asMSE-inefficiency`
+[1] 1.044142
+
+>
+>
+>
+> cleanEx(); nameEx("lowerCaseRadius")
+> ### * lowerCaseRadius
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: lowerCaseRadius
+> ### Title: Computation of the lower case radius
+> ### Aliases: lowerCaseRadius lowerCaseRadius-methods
+> ### lowerCaseRadius,L2ParamFamily,ContNeighborhood,asMSE,ANY-method
+> ### lowerCaseRadius,L2ParamFamily,TotalVarNeighborhood,asMSE,ANY-method
+> ### lowerCaseRadius,L2ParamFamily,ContNeighborhood,asMSE,onesidedBias-method
+> ### lowerCaseRadius,UnivariateDistribution,ContNeighborhood,asMSE,onesidedBias-method
+> ### lowerCaseRadius,L2ParamFamily,ContNeighborhood,asMSE,asymmetricBias-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> lowerCaseRadius(BinomFamily(size = 10), ContNeighborhood(), asMSE())
+lower case radius
+ 0.690335
+> lowerCaseRadius(BinomFamily(size = 10), TotalVarNeighborhood(), asMSE())
+lower case radius
+ 0.3451675
+>
+>
+>
+> cleanEx(); nameEx("optIC")
+> ### * optIC
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: optIC
+> ### Title: Generic function for the computation of optimally robust ICs
+> ### Aliases: optIC optIC-methods optIC,InfRobModel,asRisk-method
+> ### optIC,InfRobModel,asUnOvShoot-method
+> ### optIC,FixRobModel,fiUnOvShoot-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> B <- BinomFamily(size = 25, prob = 0.25)
+>
+> ## classical optimal IC
+> IC0 <- optIC(model = B, risk = asCov())
+> plot(IC0) # plot IC
+> checkIC(IC0, B)
+precision of centering: -4.254490e-18
+precision of Fisher consistency:
+ prob
+prob 2.220446e-16
+maximum deviation
+ 2.220446e-16
+>
+>
+>
+> cleanEx(); nameEx("optRisk")
+> ### * optRisk
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: optRisk
+> ### Title: Generic function for the computation of the minimal risk
+> ### Aliases: optRisk optRisk-methods optRisk,L2ParamFamily,asCov-method
+> ### optRisk,InfRobModel,asRisk-method
+> ### optRisk,FixRobModel,fiUnOvShoot-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> optRisk(model = NormLocationScaleFamily(), risk = asCov())
+$asCov
+ mean sd
+mean 1 0.0
+sd 0 0.5
+
+>
+>
+>
+> cleanEx(); nameEx("radiusMinimaxIC")
+> ### * radiusMinimaxIC
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: radiusMinimaxIC
+> ### Title: Generic function for the computation of the radius minimax IC
+> ### Aliases: radiusMinimaxIC radiusMinimaxIC-methods
+> ### radiusMinimaxIC,L2ParamFamily,UncondNeighborhood,asGRisk-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> N <- NormLocationFamily(mean=0, sd=1)
+> radIC <- radiusMinimaxIC(L2Fam=N, neighbor=ContNeighborhood(),
++ risk=asMSE(), loRad=0.1, upRad=0.5)
+> checkIC(radIC)
+precision of centering: -8.135927e-16
+precision of Fisher consistency:
+ mean
+mean 2.326918e-06
+maximum deviation
+ 2.326918e-06
+>
+>
+>
+> cleanEx(); nameEx("roptest")
+> ### * roptest
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: roptest
+> ### Title: Optimally robust estimation
+> ### Aliases: roptest
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> #############################
+> ## 1. Binomial data
+> #############################
+> ## generate a sample of contaminated data
+> ind <- rbinom(100, size=1, prob=0.05)
+> x <- rbinom(100, size=25, prob=(1-ind)*0.25 + ind*0.9)
+>
+> ## ML-estimate
+> MLest <- MLEstimator(x, BinomFamily(size = 25))
+> estimate(MLest)
+[1] 0.2684
+> confint(MLest)
+A[n] asymptotic (CLT-based) confidence interval:
+ 2.5 % 97.5 %
+[1,] 0.2510297 0.2857703
+Type of estimator: Maximum likelihood estimate
+samplesize: 100
+Call by which estimate was produced:
+MLEstimator(x = x, ParamFamily = BinomFamily(size = 25))
+Fixed part of the parameter at which estimate was produced:
+size
+ 25
+>
+> ## compute optimally robust estimator (known contamination)
+> robest1 <- roptest(x, BinomFamily(size = 25), eps = 0.05, steps = 3)
+> estimate(robest1)
+ prob
+0.2564328
+> confint(robest1, method = symmetricBias())
+A[n] asymptotic (LAN-based), uniform (bias-aware)
+ confidence interval:
+for symmetric Bias
+ 2.5 % 97.5 %
+prob 0.2376412 0.2752244
+Type of estimator: 3-step estimate
+samplesize: 100
+Call by which estimate was produced:
+roptest(x = x, L2Fam = BinomFamily(size = 25), eps = 0.05, steps = 3)
+Fixed part of the parameter at which estimate was produced:
+size
+ 25
+> ## neglecting bias
+> confint(robest1)
+A[n] asymptotic (LAN-based) confidence interval:
+ 2.5 % 97.5 %
+prob 0.2382146 0.2746511
+Type of estimator: 3-step estimate
+samplesize: 100
+Call by which estimate was produced:
+roptest(x = x, L2Fam = BinomFamily(size = 25), eps = 0.05, steps = 3)
+Fixed part of the parameter at which estimate was produced:
+size
+ 25
+> plot(pIC(robest1))
+> qqplot(x, robest1, cex.pch=1.5, exp.cex2.pch = -.25,
++ exp.fadcol.pch = .55, jit.fac=.9)
+$x
+ [1] 2.000000 2.859170 2.945003 3.072887 3.127441 3.867602 3.879563
+ [8] 3.969417 3.974350 3.991833 3.999465 4.003945 4.006687 4.022602
+ [15] 4.037292 4.114708 4.136915 4.173363 4.879410 4.885243 4.898712
+ [22] 4.901497 4.909182 4.912744 4.930320 4.954232 5.002498 5.006047
+ [29] 5.028207 5.091694 5.094311 5.094760 5.830003 5.859251 5.866496
+ [36] 5.869906 5.871337 5.875739 5.914491 5.942393 5.957418 5.960428
+ [43] 5.989444 5.994596 6.003645 6.008635 6.019764 6.053849 6.068266
+ [50] 6.103515 6.152629 6.155848 6.159465 6.161268 6.836726 6.837265
+ [57] 6.843205 6.867310 6.867319 6.885221 6.912581 6.916303 6.927515
+ [64] 6.930985 6.935737 6.970428 6.974665 6.982979 6.985635 7.043348
+ [71] 7.047311 7.078457 7.163208 7.856051 7.881048 7.889195 7.895933
+ [78] 7.934172 7.935981 7.937725 7.954303 8.065319 8.147733 8.842397
+ [85] 8.867768 8.880952 8.899670 8.912292 8.920068 8.954815 9.009832
+ [92] 9.097465 9.156489 10.000000 11.068189 11.166108 11.916822 12.163344
+ [99] 20.000000 24.000000
+
+$y
+ [1] 0.8557009 1.8381583 1.8628049 2.8541488 2.9861587 2.9973346
+ [7] 3.0625364 3.1545314 3.8446408 3.8708120 3.8834863 3.8895916
+ [13] 3.9161963 3.9550780 3.9641619 4.0791690 4.1128367 4.1228866
+ [19] 4.1767957 4.8499210 4.9164361 4.9324963 4.9473983 4.9660483
+ [25] 4.9982006 5.0448971 5.0592626 5.0943746 5.1015856 5.1124298
+ [31] 5.1278296 5.1632774 5.1689152 5.1750722 5.1785879 5.8477746
+ [37] 5.8764171 5.8818480 5.8835227 5.9169458 5.9256980 5.9509309
+ [43] 5.9529082 5.9583494 5.9637664 5.9793333 5.9889303 6.0151670
+ [49] 6.0295939 6.0634191 6.1123673 6.1692584 6.1762199 6.8429689
+ [55] 6.8496061 6.8592763 6.9196659 6.9218382 6.9399801 6.9705958
+ [61] 6.9898951 6.9950137 7.0313327 7.0811459 7.1024366 7.1212236
+ [67] 7.1214700 7.1252376 7.1391252 7.1731665 7.8456154 7.8837900
+ [73] 7.8998530 7.9029679 7.9400675 7.9712777 7.9772952 7.9870265
+ [79] 8.0223836 8.0715768 8.0730003 8.1313307 8.1542540 8.9035780
+ [85] 8.9066922 8.9746968 8.9975947 9.0231350 9.0562184 9.1068610
+ [91] 9.1194018 9.1722795 9.8608628 9.8717057 9.8730362 10.1667923
+ [97] 10.8757464 11.0025328 11.1530828 11.9453887
+
+>
+> ## compute optimally robust estimator (unknown contamination)
+> robest2 <- roptest(x, BinomFamily(size = 25), eps.lower = 0, eps.upper = 0.2, steps = 3)
+> estimate(robest2)
+ prob
+0.2563208
+> confint(robest2, method = symmetricBias())
+A[n] asymptotic (LAN-based), uniform (bias-aware)
+ confidence interval:
+for symmetric Bias
+ 2.5 % 97.5 %
+prob 0.2373697 0.2752720
+Type of estimator: 3-step estimate
+samplesize: 100
+Call by which estimate was produced:
+roptest(x = x, L2Fam = BinomFamily(size = 25), eps.lower = 0,
+ eps.upper = 0.2, steps = 3)
+Fixed part of the parameter at which estimate was produced:
+size
+ 25
+> plot(pIC(robest2))
+>
+> ## total variation neighborhoods (known deviation)
+> robest3 <- roptest(x, BinomFamily(size = 25), eps = 0.025,
++ neighbor = TotalVarNeighborhood(), steps = 3)
+> estimate(robest3)
+ prob
+0.2563266
+> confint(robest3, method = symmetricBias())
+A[n] asymptotic (LAN-based), uniform (bias-aware)
+ confidence interval:
+for symmetric Bias
+ 2.5 % 97.5 %
+prob 0.2317268 0.2809265
+Type of estimator: 3-step estimate
+samplesize: 100
+Call by which estimate was produced:
+roptest(x = x, L2Fam = BinomFamily(size = 25), eps = 0.025, neighbor = TotalVarNeighborhood(),
+ steps = 3)
+Fixed part of the parameter at which estimate was produced:
+size
+ 25
+> plot(pIC(robest3))
+>
+> ## total variation neighborhoods (unknown deviation)
+> robest4 <- roptest(x, BinomFamily(size = 25), eps.lower = 0, eps.upper = 0.1,
++ neighbor = TotalVarNeighborhood(), steps = 3)
+> estimate(robest4)
+ prob
+0.2561783
+> confint(robest4, method = symmetricBias())
+A[n] asymptotic (LAN-based), uniform (bias-aware)
+ confidence interval:
+for symmetric Bias
+ 2.5 % 97.5 %
+prob 0.2303988 0.2819579
+Type of estimator: 3-step estimate
+samplesize: 100
+Call by which estimate was produced:
+roptest(x = x, L2Fam = BinomFamily(size = 25), eps.lower = 0,
+ eps.upper = 0.1, neighbor = TotalVarNeighborhood(), steps = 3)
+Fixed part of the parameter at which estimate was produced:
+size
+ 25
+> plot(pIC(robest4))
+>
+>
+> #############################
+> ## 2. Poisson data
+> #############################
+> ## 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
+> MLest <- MLEstimator(x, PoisFamily())
+> estimate(MLest)
+[1] 3.871549
+> confint(MLest)
+A[n] asymptotic (CLT-based) confidence interval:
+ 2.5 % 97.5 %
+[1,] 3.796033 3.947065
+Type of estimator: Maximum likelihood estimate
+samplesize: 2608
+Call by which estimate was produced:
+MLEstimator(x = x, ParamFamily = PoisFamily())
+>
+> ## compute optimally robust estimator (unknown contamination)
+> robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
+> estimate(robest)
+ lambda
+3.908322
+> confint(robest, symmetricBias())
+A[n] asymptotic (LAN-based), uniform (bias-aware)
+ confidence interval:
+for symmetric Bias
+ 2.5 % 97.5 %
+lambda 3.759634 4.05701
+Type of estimator: 3-step estimate
+samplesize: 2608
+Call by which estimate was produced:
+roptest(x = x, L2Fam = PoisFamily(), eps.upper = 0.1, steps = 3)
+> plot(pIC(robest))
+> qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
++ exp.fadcol.pch = .55, jit.fac=.9)
+$x
+ [1] -0.1793041795 -0.1759861769 -0.1650497656 -0.1612263552 -0.1604914454
+ [6] -0.1566180292 -0.1547882355 -0.1539141720 -0.1492880379 -0.1421819501
+ [11] -0.1354595166 -0.1284600289 -0.1216407305 -0.1187123112 -0.0860652946
+ [16] -0.0848703421 -0.0741629524 -0.0691011184 -0.0559845383 -0.0513339317
+ [21] -0.0480370981 -0.0467019431 -0.0436466014 -0.0339251104 -0.0276845006
+ [26] -0.0235016493 -0.0210747085 -0.0206963610 -0.0088567486 -0.0087029861
+ [31] -0.0064189536 -0.0019243706 0.0003061754 0.0053037550 0.0136313722
+ [36] 0.0281382900 0.0373652645 0.0395096166 0.0568181049 0.0586839484
+ [41] 0.0702044087 0.0741092423 0.0862710285 0.0891486229 0.1054472486
+ [46] 0.1086075740 0.1162055900 0.1218182640 0.1365959866 0.1513864226
+ [51] 0.1557258942 0.1568564884 0.1585111364 0.1593019161 0.1671541337
+ [56] 0.1753566350 0.1777497214 0.8206612690 0.8229262439 0.8239729856
+ [61] 0.8281868407 0.8305757796 0.8310723656 0.8326999961 0.8355585732
+ [66] 0.8378752902 0.8398591431 0.8406541642 0.8409931813 0.8433641932
+ [71] 0.8476829337 0.8538565200 0.8539804964 0.8542547681 0.8552110735
+ [76] 0.8583933951 0.8615232369 0.8621911180 0.8623794204 0.8661980350
+ [81] 0.8678180127 0.8682800816 0.8691355310 0.8730931763 0.8750037309
+ [86] 0.8759444402 0.8770225444 0.8787107722 0.8791575668 0.8825149192
+ [91] 0.8874734936 0.8883207704 0.8897622658 0.8909728169 0.8917991858
+ [96] 0.8927138863 0.8965523245 0.9008669832 0.9064418583 0.9069685200
+ [101] 0.9074643792 0.9107235271 0.9121536095 0.9199569112 0.9210700769
+ [106] 0.9254615402 0.9286095774 0.9331126588 0.9337162284 0.9369514506
+ [111] 0.9383477380 0.9407154735 0.9411203288 0.9412761912 0.9424837941
+ [116] 0.9430515644 0.9446183559 0.9453607844 0.9454781263 0.9462252053
+ [121] 0.9483909064 0.9502819066 0.9533115229 0.9535180736 0.9538464654
+ [126] 0.9565396211 0.9567541329 0.9577016644 0.9597082560 0.9612517187
+ [131] 0.9618216605 0.9629732560 0.9632237059 0.9636769201 0.9647909426
+ [136] 0.9660845800 0.9669799935 0.9695144805 0.9706020370 0.9762511055
+ [141] 0.9769515046 0.9821747574 0.9826629447 0.9854471484 0.9859222646
+ [146] 0.9881614416 0.9900043129 0.9914276866 0.9927408860 0.9948419585
+ [151] 0.9982230780 0.9992324987 1.0008102935 1.0061235375 1.0091099813
+ [156] 1.0100975965 1.0117364390 1.0119018181 1.0139659478 1.0142063300
+ [161] 1.0151125407 1.0192512533 1.0195037420 1.0223157134 1.0243674631
+ [166] 1.0259812935 1.0316540557 1.0317039015 1.0318309085 1.0347214441
+ [171] 1.0383187581 1.0435874658 1.0438383806 1.0444368554 1.0455957451
+ [176] 1.0485552876 1.0496471404 1.0518786023 1.0525842197 1.0528045084
+ [181] 1.0534783411 1.0546987396 1.0552057995 1.0557038353 1.0569007193
+ [186] 1.0588033811 1.0603891459 1.0604982174 1.0633073538 1.0666387019
+ [191] 1.0677790855 1.0682046519 1.0684924203 1.0690939620 1.0706309198
+ [196] 1.0751464184 1.0752310714 1.0765144441 1.0772984619 1.0776999821
+ [201] 1.0779722124 1.0819961217 1.0832937684 1.0837784864 1.0854299685
+ [206] 1.0899357994 1.0905415005 1.0923921667 1.0931308505 1.0937127814
+ [211] 1.0974353074 1.1000953104 1.1052563522 1.1075732378 1.1095806777
+ [216] 1.1131306302 1.1156184345 1.1179783971 1.1182723695 1.1185123830
+ [221] 1.1185567496 1.1188884534 1.1233323652 1.1235232534 1.1246484710
+ [226] 1.1258045535 1.1258387574 1.1266003503 1.1287005533 1.1303322548
+ [231] 1.1309070039 1.1322059634 1.1334899991 1.1346938779 1.1359281024
+ [236] 1.1456367932 1.1462734941 1.1466249098 1.1489624711 1.1541439174
+ [241] 1.1543598013 1.1558111337 1.1565136419 1.1577436900 1.1578289484
+ [246] 1.1580892142 1.1591469674 1.1608544149 1.1616177439 1.1623423633
+ [251] 1.1638528258 1.1640432767 1.1646099600 1.1647491475 1.1664399911
+ [256] 1.1690301398 1.1709252868 1.1712366459 1.1778089963 1.1785475723
+ [261] 1.8204732764 1.8217159203 1.8220619154 1.8234458451 1.8235353754
+ [266] 1.8240824695 1.8242335045 1.8247194981 1.8259803959 1.8264647887
+ [271] 1.8275642634 1.8287305357 1.8298992364 1.8302214558 1.8314167534
+ [276] 1.8323979540 1.8324809808 1.8329114484 1.8330305607 1.8334325633
+ [281] 1.8340760199 1.8341064984 1.8341296256 1.8353565628 1.8354389211
+ [286] 1.8364301152 1.8365745974 1.8382776193 1.8382829313 1.8385743163
+ [291] 1.8389852783 1.8408555444 1.8415640868 1.8423093323 1.8426030066
+ [296] 1.8436887071 1.8439375001 1.8439673770 1.8462756389 1.8463688731
+ [301] 1.8474894006 1.8476356853 1.8485982895 1.8505715813 1.8509426902
+ [306] 1.8511023351 1.8537139469 1.8626083690 1.8629351539 1.8633016121
+ [311] 1.8637297387 1.8644344077 1.8652625197 1.8662832835 1.8677330875
+ [316] 1.8681444229 1.8683011182 1.8684248718 1.8695829022 1.8717634210
+ [321] 1.8727622634 1.8733361133 1.8733950716 1.8749603235 1.8767367499
+ [326] 1.8769591188 1.8775225362 1.8781746053 1.8786758882 1.8793608127
+ [331] 1.8795494441 1.8800163800 1.8803774929 1.8828782044 1.8839582308
+ [336] 1.8840461893 1.8840618859 1.8875681647 1.8876482538 1.8877227997
+ [341] 1.8877387876 1.8888534042 1.8892271707 1.8906684209 1.8930484684
+ [346] 1.8938831355 1.8942124181 1.8948940448 1.8951082311 1.8984160829
+ [351] 1.8992087852 1.9005563803 1.9010480251 1.9012742605 1.9029661751
+ [356] 1.9045696850 1.9054153473 1.9059453211 1.9062694022 1.9064443728
+ [361] 1.9073838774 1.9092419052 1.9130968269 1.9138533916 1.9144755892
+ [366] 1.9145133773 1.9152634260 1.9155932192 1.9157279268 1.9176939555
+ [371] 1.9178774602 1.9181667092 1.9182009139 1.9183718870 1.9196035363
+ [376] 1.9215690668 1.9220517955 1.9240112496 1.9252393022 1.9267345273
+ [381] 1.9271700044 1.9273381506 1.9288752794 1.9300507191 1.9312144634
+ [386] 1.9323331110 1.9370939655 1.9381368730 1.9390405114 1.9395937806
+ [391] 1.9400979854 1.9408684247 1.9410955780 1.9419045590 1.9433876507
+ [396] 1.9435531289 1.9452610885 1.9453753044 1.9455466912 1.9456205729
+ [401] 1.9466000197 1.9472908871 1.9485009776 1.9485849387 1.9491067149
+ [406] 1.9497562556 1.9504273820 1.9521103555 1.9528701046 1.9573875734
+ [411] 1.9579820217 1.9581457537 1.9589958679 1.9593644916 1.9631741931
+ [416] 1.9635124504 1.9643036342 1.9645198832 1.9667773469 1.9680159552
+ [421] 1.9697096891 1.9711903952 1.9717205890 1.9721342569 1.9724810306
+ [426] 1.9726952694 1.9733953163 1.9742049205 1.9742511371 1.9753241527
+ [431] 1.9757983269 1.9761686343 1.9765642240 1.9765978973 1.9777366630
+ [436] 1.9778744244 1.9791235228 1.9791770324 1.9791782087 1.9814338874
+ [441] 1.9836555579 1.9842110242 1.9842828375 1.9865828989 1.9889764535
+ [446] 1.9892463024 1.9900956516 1.9901209215 1.9904611154 1.9906616348
+ [451] 1.9918867559 1.9919821668 1.9931476305 1.9959921136 1.9963072301
+ [456] 1.9963865439 1.9964102330 1.9973777006 1.9988066792 2.0005891198
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/robast -r 388
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