[Robast-commits] r1200 - in branches/robast-1.2/pkg: ROptEst/tests/Examples RandVar/tests/Examples RobAStBase/inst RobAStBase/tests/Examples RobExtremes/tests RobLox/tests/Examples RobLoxBioC/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Mar 11 13:13:45 CET 2019
Author: ruckdeschel
Date: 2019-03-11 13:13:45 +0100 (Mon, 11 Mar 2019)
New Revision: 1200
Added:
branches/robast-1.2/pkg/RobAStBase/inst/chkTimeCode/
branches/robast-1.2/pkg/RobExtremes/tests/Examples/
Removed:
branches/robast-1.2/pkg/RobAStBase/inst/chkTimeCode/
Modified:
branches/robast-1.2/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
branches/robast-1.2/pkg/RandVar/tests/Examples/RandVar-Ex.Rout.save
branches/robast-1.2/pkg/RobAStBase/tests/Examples/RobAStBase-Ex.Rout.save
branches/robast-1.2/pkg/RobLox/tests/Examples/RobLox-Ex.Rout.save
branches/robast-1.2/pkg/RobLoxBioC/tests/Examples/RobLoxBioC-Ex.Rout.save
Log:
+ RobASt: branch 1.2 copied -Ex.Rout.save files from trunk; minor name change in RobAStBase/inst/chkTimeCode (naming)
Modified: branches/robast-1.2/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
===================================================================
--- branches/robast-1.2/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save 2019-03-11 08:10:35 UTC (rev 1199)
+++ branches/robast-1.2/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save 2019-03-11 12:13:45 UTC (rev 1200)
@@ -1,1166 +1,1677 @@
-
-R version 2.12.1 Patched (2011-01-04 r53913)
-Copyright (C) 2011 The R Foundation for Statistical Computing
-ISBN 3-900051-07-0
-Platform: x86_64-unknown-linux-gnu (64-bit)
-
-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.
-
-> pkgname <- "ROptEst"
-> source(file.path(R.home("share"), "R", "examples-header.R"))
-> options(warn = 1)
-> library('ROptEst')
-Loading required package: distr
-Loading required package: startupmsg
-:startupmsg> Utilities for start-up messages (version 0.7.1)
-:startupmsg>
-:startupmsg> For more information see ?"startupmsg",
-:startupmsg> NEWS("startupmsg")
-
-Loading required package: sfsmisc
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-:SweaveListingUtils> Utilities for Sweave together with
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-:SweaveListingUtils>
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-: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
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-:distr> Object oriented implementation of distributions (version
-:distr> 2.3)
-:distr>
-:distr> Attention: Arithmetics on distribution objects are
-:distr> understood as operations on corresponding random variables
-:distr> (r.v.s); see distrARITH().
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-: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".
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-:distr> For more information see ?"distr", NEWS("distr"), as well as
-:distr> http://distr.r-forge.r-project.org/
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-: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").
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-:distrMod> Object oriented implementation of probability models
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-:distrMod> distrModoptions() ---c.f. ?"distrModoptions".
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-:distrMod> NEWS("distrMod"), as well as
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-:distrMod> vignette("distrMod").
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->
-> assign(".oldSearch", search(), 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
-3.908135
-> ## check influence curve
-> checkIC(pIC(robest))
-precision of centering: -2.707017e-08
-precision of Fisher consistency:
- lambda
-lambda -1.980378e-06
-maximum deviation
- 1.980378e-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 3.826169 3.990102
-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 3.761616 4.054655
-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("asAnscombe-class")
-> ### * asAnscombe-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: asAnscombe-class
-> ### Title: Asymptotic Anscombe risk
-> ### Aliases: asAnscombe-class eff eff,asAnscombe-method
-> ### show,asAnscombe-method
-> ### Keywords: classes
->
-> ### ** Examples
->
-> new("asAnscombe")
-An object of class “asAnscombe”
-risk type: optimal bias robust IC for given ARE in the ideal model
-ARE in the ideal model: 0.95
->
->
->
-> cleanEx()
-> nameEx("asAnscombe")
-> ### * asAnscombe
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: asAnscombe
-> ### Title: Generating function for asAnscombe-class
-> ### Aliases: asAnscombe
-> ### Keywords: robust
->
-> ### ** Examples
->
-> asAnscombe()
-An object of class “asAnscombe”
-risk type: optimal bias robust IC for given ARE in the ideal model
-ARE in the ideal model: 0.95
->
-> ## The function is currently defined as
-> function(eff = .95, biastype = symmetricBias(), normtype = NormType()){
-+ new("asAnscombe", eff = eff, biastype = biastype, normtype = normtype) }
-function (eff = 0.95, biastype = symmetricBias(), normtype = NormType())
-{
- new("asAnscombe", eff = eff, biastype = biastype, normtype = normtype)
-}
->
->
->
-> cleanEx()
-> nameEx("asL1-class")
-> ### * asL1-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: asL1-class
-> ### Title: Asymptotic mean absolute error
-> ### Aliases: asL1-class
-> ### Keywords: classes
->
-> ### ** Examples
->
-> new("asMSE")
-An object of class “asMSE”
-risk type: asymptotic mean square error
->
->
->
-> cleanEx()
-> nameEx("asL1")
-> ### * asL1
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: asL1
-> ### Title: Generating function for asMSE-class
-> ### Aliases: asL1
-> ### Keywords: robust
->
-> ### ** Examples
->
-> asL1()
-An object of class “asL1”
-risk type: asymptotic mean absolute error
->
-> ## The function is currently defined as
-> function(biastype = symmetricBias(), normtype = NormType()){
-+ new("asL1", biastype = biastype, normtype = normtype) }
-function (biastype = symmetricBias(), normtype = NormType())
-{
- new("asL1", biastype = biastype, normtype = normtype)
-}
->
->
->
-> cleanEx()
-> nameEx("asL4-class")
-> ### * asL4-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: asL4-class
-> ### Title: Asymptotic mean power 4 error
-> ### Aliases: asL4-class
-> ### Keywords: classes
->
-> ### ** Examples
->
-> new("asMSE")
-An object of class “asMSE”
-risk type: asymptotic mean square error
->
->
->
-> cleanEx()
-> nameEx("asL4")
-> ### * asL4
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: asL4
-> ### Title: Generating function for asL4-class
-> ### Aliases: asL4
-> ### Keywords: robust
->
-> ### ** Examples
->
-> asL4()
-An object of class “asL4”
-risk type: asymptotic mean power 4 error
->
-> ## The function is currently defined as
-> function(biastype = symmetricBias(), normtype = NormType()){
-+ new("asL4", biastype = biastype, normtype = normtype) }
-function (biastype = symmetricBias(), normtype = NormType())
-{
- new("asL4", biastype = biastype, normtype = normtype)
-}
->
->
->
-> 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("getMaxIneff")
-> ### * getMaxIneff
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: getMaxIneff
-> ### Title: getMaxIneff - computation of the maximal inefficiency of an IC
-> ### Aliases: getMaxIneff
-> ### Keywords: robust
->
-> ### ** Examples
->
-> N0 <- NormLocationFamily(mean=2, sd=3)
-> ## L_2 family + infinitesimal neighborhood
-> neighbor <- ContNeighborhood(radius = 0.5)
-> N0.Rob1 <- InfRobModel(center = N0, neighbor = neighbor)
-> ## OBRE solution (ARE 95%)
-> N0.ICA <- optIC(model = N0.Rob1, risk = asAnscombe(.95))
-minimal bound: 3.759947
-minimal bound: 3.759947
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.0009839269
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.045311
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.0389404
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04095049
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04096877
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04096873
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04096877
-> ## OMSE solution radius 0.5
-> N0.ICM <- optIC(model=N0.Rob1, risk=asMSE())
-> ## RMX solution
-> N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
->
-> getMaxIneff(N0.ICA,neighbor)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 1.658389
-> getMaxIneff(N0.ICM,neighbor)
-[1] 1.265537
-> getMaxIneff(N0.ICR,neighbor)
-[1] 1.180746
->
-> N0ls <- NormLocationScaleFamily()
-> ICsc <- makeIC(list(sin,cos),N0ls)
-> getMaxIneff(ICsc,neighbor)
-Warning in A[DA.comp] <- matrix(param[1:lA.comp], ncol = k, nrow = p) :
- number of items to replace is not a multiple of replacement length
-[1] 2.679436
->
->
->
->
-> cleanEx()
-> nameEx("getReq")
-> ### * getReq
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: getReq
-> ### Title: getReq - computation of the radius interval where IC1 is better
-> ### than IC2
-> ### Aliases: getReq
-> ### Keywords: robust
->
-> ### ** Examples
->
-> N0 <- NormLocationFamily(mean=2, sd=3)
-> ## L_2 family + infinitesimal neighborhood
-> neighbor <- ContNeighborhood(radius = 0.5)
-> N0.Rob1 <- InfRobModel(center = N0, neighbor = neighbor)
-> ## OBRE solution (ARE 95%)
-> N0.ICA <- optIC(model = N0.Rob1, risk = asAnscombe(.95))
-minimal bound: 3.759947
-minimal bound: 3.759947
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.0009839269
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.045311
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.0389404
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04095049
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04096877
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04096873
-minimal bound: 3.759947
-maximum iterations reached!
- achieved precision: 0.04096877
-> ## MSE solution
-> N0.ICM <- optIC(model=N0.Rob1, risk=asMSE())
-> ## RMX solution
-> N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
->
-> getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=1)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.0000000 0.3750825
-> getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.06848038
-> getReq(asL1(),neighbor,N0.ICA,N0.ICM,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.06544434
-> getReq(asL4(),neighbor,N0.ICA,N0.ICM,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.0000000 0.0754216
-> getReq(asMSE(),neighbor,N0.ICA,N0.ICR,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.07544307
-> getReq(asL1(),neighbor,N0.ICA,N0.ICR,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.07161849
-> getReq(asL4(),neighbor,N0.ICA,N0.ICR,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.08429762
-> getReq(asMSE(),neighbor,N0.ICM,N0.ICR,n=30)
-[1] 0.0000000 0.1016517
->
-> ### when to use MAD and when Qn
-> ## for Qn, see C. Croux, P. Rousseeuw (1993). Alternatives to the Median
-> ## Absolute Deviation, JASA 88(424):1273-1283
-> L2M <- NormScaleFamily()
-> IC.mad <- makeIC(function(x)sign(abs(x)-qnorm(.75)),L2M)
-$Curve
-An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
-
-$CallL2Fam
-L2Fam at fam.call
-
-An object of class “IC”
-### name: square integrable (partial) influence curve
-### L2-differentiable parametric family: normal scale family
-
-### 'Curve': An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
-
-### Infos:
- method message
-> d.qn <- (2^.5*qnorm(5/8))^-1
-> IC.qn <- makeIC(function(x) d.qn*(1/4 - pnorm(x+1/d.qn) + pnorm(x-1/d.qn)), L2M)
-$Curve
-An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
-
-$CallL2Fam
-L2Fam at fam.call
-
-An object of class “IC”
-### name: square integrable (partial) influence curve
-### L2-differentiable parametric family: normal scale family
-
-### 'Curve': An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
-
-### Infos:
- method message
-> getReq(asMSE(), neighbor, IC.mad, IC.qn)
-[1] 0.5074459 Inf
-> # => MAD is better once r > 0.5144 (i.e. for more than 2 outliers for n = 30)
->
->
->
-> 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 -3.502745e-06
-maximum deviation
- 3.502745e-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
-0.2564327
-> confint(robest1, method = symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-prob 0.237641 0.2752245
-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 0.2382143 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))
-> qq1 <- qqplot(x, robest1, cex.pch=1.5, exp.cex2.pch = -.25,
-+ exp.fadcol.pch = .55, jit.fac=.9)
-> str(qq1)
-List of 2
- $ x: num [1:100] 2 2.86 2.95 3.07 3.13 ...
- $ y: num [1:100] 0.856 1.838 1.863 2.854 2.986 ...
->
-> ## compute optimally robust estimator (unknown contamination)
-> robest2 <- roptest(x, BinomFamily(size = 25), eps.lower = 0, eps.upper = 0.2, steps = 3)
-> estimate(robest2)
- prob
-0.2564060
-> confint(robest2, method = symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-prob 0.2375772 0.2752347
-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
-0.2563265
-> confint(robest3, method = symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-prob 0.2375738 0.2750792
-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
-0.2563281
-> confint(robest4, method = symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-prob 0.2375777 0.2750785
-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
-3.908135
-> confint(robest, symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-lambda 3.761616 4.054655
-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))
-> qq2 <- qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
-+ exp.fadcol.pch = .55, jit.fac=.9)
-> str(qq2)
-List of 2
- $ x: num [1:2608] -0.179 -0.176 -0.165 -0.161 -0.16 ...
- $ y: num [1:2608] -0.179 -0.179 -0.177 -0.17 -0.167 ...
->
-> ## total variation neighborhoods (unknown deviation)
-> robest1 <- roptest(x, PoisFamily(), eps.upper = 0.05,
-+ neighbor = TotalVarNeighborhood(), steps = 3)
-> estimate(robest1)
- lambda
-3.900709
-> confint(robest1, symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-lambda 3.75276 4.048659
-Type of estimator: 3-step estimate
-samplesize: 2608
-Call by which estimate was produced:
-roptest(x = x, L2Fam = PoisFamily(), eps.upper = 0.05, neighbor = TotalVarNeighborhood(),
- steps = 3)
-> plot(pIC(robest1))
->
->
-> #############################
-> ## 3. Normal (Gaussian) location and scale
-> #############################
-> ## 24 determinations of copper in wholemeal flour
-> library(MASS)
-> data(chem)
-> plot(chem, main = "copper in wholemeal flour", pch = 20)
->
-> ## ML-estimate
-> MLest <- MLEstimator(chem, NormLocationScaleFamily())
-> estimate(MLest)
- mean sd
-4.280417 5.185859
-> confint(MLest)
-A[n] asymptotic (CLT-based) confidence interval:
- 2.5 % 97.5 %
-mean 2.205679 6.355154
-sd 3.718798 6.652920
-Type of estimator: Maximum likelihood estimate
-samplesize: 24
-Call by which estimate was produced:
-MLEstimator(x = chem, ParamFamily = NormLocationScaleFamily())
->
-> ## compute optimally robust estimator (known contamination)
-> ## takes some time -> you can use package RobLox for normal
-> ## location and scale which is optimized for speed
-> robest <- roptest(chem, NormLocationScaleFamily(), eps = 0.05, steps = 3)
-> estimate(robest)
- mean sd
-3.143156 0.667100
-> confint(robest, symmetricBias())
-A[n] asymptotic (LAN-based), uniform (bias-aware)
- confidence interval:
-for symmetric Bias
- 2.5 % 97.5 %
-mean 2.8118305 3.474482
-sd 0.4223911 0.911809
-Type of estimator: 3-step estimate
-samplesize: 24
-Call by which estimate was produced:
[TRUNCATED]
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svnlook diff /svnroot/robast -r 1200
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