[Robast-commits] r125 - in pkg: ROptEst ROptEst/R ROptEst/inst/scripts ROptRegTS RandVar RandVar/R RandVar/inst/doc RobAStBase RobAStBase/R RobAStBase/man RobLox RobLox/R RobLox/man RobRex
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Jul 24 19:07:22 CEST 2008
Author: stamats
Date: 2008-07-24 19:07:22 +0200 (Thu, 24 Jul 2008)
New Revision: 125
Removed:
pkg/RandVar/inst/doc/RandVar.aux
pkg/RandVar/inst/doc/RandVar.brf
pkg/RandVar/inst/doc/RandVar.log
pkg/RandVar/inst/doc/RandVar.out
pkg/RandVar/inst/doc/RandVar.tex
pkg/RandVar/inst/doc/RandVar.toc
Modified:
pkg/ROptEst/DESCRIPTION
pkg/ROptEst/R/getFixRobIC_fiUnOvShoot.R
pkg/ROptEst/R/getInfRobIC_asUnOvShoot.R
pkg/ROptEst/R/getRiskIC.R
pkg/ROptEst/inst/scripts/BinomialModel.R
pkg/ROptEst/inst/scripts/ExponentialScaleModel.R
pkg/ROptEst/inst/scripts/GammaModel.R
pkg/ROptEst/inst/scripts/GumbelLocationModel.R
pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R
pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R
pkg/ROptEst/inst/scripts/NormalScaleModel.R
pkg/ROptEst/inst/scripts/PoissonModel.R
pkg/ROptEst/inst/scripts/UnderOverShootRisk.R
pkg/ROptRegTS/DESCRIPTION
pkg/RandVar/DESCRIPTION
pkg/RandVar/R/EuclRandVariable.R
pkg/RandVar/inst/doc/RandVar.pdf
pkg/RobAStBase/DESCRIPTION
pkg/RobAStBase/R/AllClass.R
pkg/RobAStBase/R/AllGeneric.R
pkg/RobAStBase/R/ContIC.R
pkg/RobAStBase/R/TotalVarIC.R
pkg/RobAStBase/R/Weights.R
pkg/RobAStBase/R/getRiskIC.R
pkg/RobAStBase/R/infoPlot.R
pkg/RobAStBase/man/BdStWeight-class.Rd
pkg/RobAStBase/man/BoundedWeight-class.Rd
pkg/RobAStBase/man/ContIC-class.Rd
pkg/RobAStBase/man/HampelWeight-class.Rd
pkg/RobAStBase/man/RobWeight-class.Rd
pkg/RobLox/DESCRIPTION
pkg/RobLox/R/rlOptIC.R
pkg/RobLox/R/rlsOptIC_AL.R
pkg/RobLox/R/roblox.R
pkg/RobLox/R/rowRoblox.R
pkg/RobLox/R/rsOptIC.R
pkg/RobLox/man/roblox.Rd
pkg/RobLox/man/rowRoblox.Rd
pkg/RobRex/DESCRIPTION
Log:
merged branch into trunk
Modified: pkg/ROptEst/DESCRIPTION
===================================================================
--- pkg/ROptEst/DESCRIPTION 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/DESCRIPTION 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,13 +1,12 @@
Package: ROptEst
Version: 0.6.0
-Date: 2008-02-14
+Date: 2008-07-21
Title: Optimally robust estimation
Description: Optimally robust estimation using S4 classes and methods
Depends: R(>= 2.4.0), methods, distr(>= 2.0), distrEx(>= 2.0), distrMod(>= 2.0), RandVar(>= 0.6.2), RobAStBase
-Author: Matthias Kohl
+Author: Matthias Kohl, Peter Ruckdeschel
Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
-SaveImage: no
LazyLoad: yes
License: GPL version 2 or later
-URL: http://www.stamats.de/RobASt.htm
+URL: http://robast.r-forge.r-project.org/
Packaged: Thu Jan 3 20:00:08 2008; btm722
Modified: pkg/ROptEst/R/getFixRobIC_fiUnOvShoot.R
===================================================================
--- pkg/ROptEst/R/getFixRobIC_fiUnOvShoot.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/R/getFixRobIC_fiUnOvShoot.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -29,9 +29,16 @@
a <- -A*c0
b <- 2*A*c0
+ w <- new("BdStWeight")
+ clip(w) <- c(0,b)+a
+ stand(w) <- as.matrix(A)
+ weight(w) <- getweight(w, neighbor = TotalVarNeighborhood(radius = neighbor at radius),
+ biastype = symmetricBias(), normW = NormType())
+
Risk <- getFiRisk(risk = risk, Distr = Distr, neighbor = neighbor,
clip = c0, stand = A, sampleSize = sampleSize,
Algo = Algo, cont = cont)
- return(list(A = as.matrix(A), a = a, b = b, d = NULL, risk = Risk, info = info))
+ return(list(A = as.matrix(A), a = a, b = b, d = NULL, risk = Risk, info = info, w = w,
+ biastype = symmetricBias(), normtype = NormType()))
})
Modified: pkg/ROptEst/R/getInfRobIC_asUnOvShoot.R
===================================================================
--- pkg/ROptEst/R/getInfRobIC_asUnOvShoot.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/R/getInfRobIC_asUnOvShoot.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -124,9 +124,17 @@
biastype = biastype, clip = c0, cent = z, trafo = trafo)
a <- as.vector(A)*z
b <- abs(as.vector(A))*c0
+
+ w <- new("BdStWeight")
+ clip(w) <- c(0,b)+a
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = TotalVarNeighborhood(radius = neighbor at radius),
+ biastype = biastype, normW = normtype(risk))
+
Risk <- getAsRisk(risk = risk, L2deriv = L2deriv, neighbor = neighbor,
biastype = biastype, clip = b, cent = a, stand = A,
trafo = trafo)
- return(list(A = A, a = a, b = b, d = NULL, risk = Risk, info = info))
+ return(list(A = A, a = a, b = b, d = NULL, risk = Risk, info = info, w = w,
+ biastype = biastype, normtype = normtype(risk)))
})
Modified: pkg/ROptEst/R/getRiskIC.R
===================================================================
--- pkg/ROptEst/R/getRiskIC.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/R/getRiskIC.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -15,7 +15,7 @@
neighbor = "missing",
L2Fam = "L2ParamFamily"),
function(IC, risk, L2Fam){
- Cov <- IC at Risks[["asCov"]] ; print(L2Fam)
+ Cov <- IC at Risks[["asCov"]]
return(list(asCov = list(distribution = .getDistr(L2Fam), value = Cov)))
})
@@ -26,9 +26,9 @@
setMethod("getBiasIC", signature(IC = "HampIC",
neighbor = "UncondNeighborhood"),
function(IC, neighbor, L2Fam,...){
- if(missing(L2Fam))
- {misF <- TRUE;
- L2Fam <- eval(IC at CallL2Fam)}
+ if(missing(L2Fam))
+ L2Fam <- eval(IC at CallL2Fam)
+
return(list(asBias = list(distribution = .getDistr(L2Fam),
neighborhood = neighbor at type, value = IC at Risks$asBias$value)))
})
Modified: pkg/ROptEst/inst/scripts/BinomialModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/BinomialModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/BinomialModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -27,7 +27,7 @@
(RobB2 <- InfRobModel(center = B, neighbor = TotalVarNeighborhood(radius = 0.5)))
## MSE solution
-system.time(IC1 <- optIC(model=RobB1, risk=asMSE()), gcFirst = TRUE)
+system.time(IC1 <- optIC(model=RobB1, risk=asMSE()))
IC1
checkIC(IC1)
Risks(IC1)
@@ -42,7 +42,7 @@
Risks(IC1)
plot(IC1)
-system.time(IC2 <- optIC(model=RobB2, risk=asMSE()), gcFirst = TRUE)
+system.time(IC2 <- optIC(model=RobB2, risk=asMSE()))
IC2
checkIC(IC2)
Risks(IC2)
@@ -80,14 +80,14 @@
## radius minimax IC
system.time(IC7 <- radiusMinimaxIC(L2Fam=B, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=1), gcFirst = TRUE)
+ risk=asMSE(), loRad=0, upRad=1))
IC7
checkIC(IC7)
Risks(IC7)
plot(IC7)
system.time(IC8 <- radiusMinimaxIC(L2Fam=B, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=1), gcFirst = TRUE)
+ risk=asMSE(), loRad=0, upRad=1))
IC8
checkIC(IC8)
Risks(IC8)
@@ -97,10 +97,10 @@
## least favorable radius
## (may take quite some time!)
system.time(r.rho1 <- leastFavorableRadius(L2Fam=B, neighbor=ContNeighborhood(),
- risk=asMSE(), rho=0.5), gcFirst = TRUE)
+ risk=asMSE(), rho=0.5))
r.rho1
system.time(r.rho2 <- leastFavorableRadius(L2Fam=B, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), rho=0.5), gcFirst = TRUE)
+ risk=asMSE(), rho=0.5))
r.rho2
## one-step estimation
@@ -109,24 +109,24 @@
x <- rbinom(100, size=25, prob=(1-ind)*0.25 + ind*0.75)
## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=x, Binom(size=25), param = "prob"))
+(est0 <- MDEstimator(x=x, BinomFamily(size=25), interval = c(0, 1)))
## 3. one-step estimation: radius known
-RobB3 <- InfRobModel(center=BinomFamily(size=25, prob=est0$prob),
+RobB3 <- InfRobModel(center=BinomFamily(size=25, prob=est0$estimate),
neighbor=ContNeighborhood(radius=0.5))
IC9 <- optIC(model=RobB3, risk=asMSE())
-(est1 <- oneStepEstimator(x, IC=IC9, start=est0$prob))
+(est1c <- oneStepEstimator(x, IC=IC9, start=est0$estimate))
-RobB4 <- InfRobModel(center=BinomFamily(size=25, prob=est0$prob),
+RobB4 <- InfRobModel(center=BinomFamily(size=25, prob=est0$estimate),
neighbor=TotalVarNeighborhood(radius=0.25))
IC10 <- optIC(model=RobB4, risk=asMSE())
-(est1 <- oneStepEstimator(x, IC=IC10, start=est0$prob))
+(est1v <- oneStepEstimator(x, IC=IC10, start=est0$estimate))
## 4. one-step estimation: radius interval
-IC11 <- radiusMinimaxIC(L2Fam=BinomFamily(size=25, prob=est0$prob),
+IC11 <- radiusMinimaxIC(L2Fam=BinomFamily(size=25, prob=est0$estimate),
neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(est2 <- oneStepEstimator(x, IC=IC11, start=est0$prob))
+(est2c <- oneStepEstimator(x, IC=IC11, start=est0$estimate))
-IC12 <- radiusMinimaxIC(L2Fam=BinomFamily(size=25, prob=est0$prob),
+IC12 <- radiusMinimaxIC(L2Fam=BinomFamily(size=25, prob=est0$estimate),
neighbor=TotalVarNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(est2 <- oneStepEstimator(x, IC=IC12, start=est0$prob))
+(est2v <- oneStepEstimator(x, IC=IC12, start=est0$estimate))
Modified: pkg/ROptEst/inst/scripts/ExponentialScaleModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/ExponentialScaleModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/ExponentialScaleModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -3,25 +3,25 @@
###############################################################################
require(ROptEst)
-## generates Exponential Scale Family with rate = 1
-E1 <- ExpScaleFamily(rate = 1)
+## generates Exponential Scale Family with rate = 2
+E1 <- ExpScaleFamily(rate = 2)
E1 # show E1
plot(E1) # plot of Exp(rate = 1) and L_2 derivative
checkL2deriv(E1)
-# classical optimal IC
+## classical optimal IC
E1.IC0 <- optIC(model = E1, risk = asCov())
E1.IC0 # show IC
checkIC(E1.IC0)
Risks(E1.IC0)
plot(E1.IC0) # plot IC
-# L_2 family + infinitesimal neighborhood
+## L_2 family + infinitesimal neighborhood
E1.Rob1 <- InfRobModel(center = E1, neighbor = ContNeighborhood(radius = 0.5))
E1.Rob1 # show E1.Rob1
E1.Rob2 <- InfRobModel(center = E1, neighbor = TotalVarNeighborhood(radius = 0.5))
-# MSE solution
+## MSE solution
(E1.IC1 <- optIC(model=E1.Rob1, risk=asMSE()))
checkIC(E1.IC1)
Risks(E1.IC1)
@@ -31,7 +31,7 @@
Risks(E1.IC2)
plot(E1.IC2)
-# lower case solutions
+## lower case solutions
(E1.IC3 <- optIC(model=E1.Rob1, risk=asBias()))
checkIC(E1.IC3)
Risks(E1.IC3)
@@ -41,7 +41,7 @@
Risks(E1.IC4)
plot(E1.IC4)
-# Hampel solution
+## Hampel solution
(E1.IC5 <- optIC(model=E1.Rob1, risk=asHampel(bound=clip(E1.IC1))))
checkIC(E1.IC5)
Risks(E1.IC5)
@@ -51,7 +51,7 @@
Risks(E1.IC6)
plot(E1.IC6)
-# radius minimax IC
+## radius minimax IC
(E1.IC7 <- radiusMinimaxIC(L2Fam=E1, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=0.5))
checkIC(E1.IC7)
@@ -61,8 +61,8 @@
checkIC(E1.IC8)
Risks(E1.IC8)
-# least favorable radius
-# (may take quite some time!)
+## least favorable radius
+## (may take quite some time!)
(E1.r.rho1 <- leastFavorableRadius(L2Fam=E1, neighbor=ContNeighborhood(),
risk=asMSE(), rho=0.5))
(E1.r.rho2 <- leastFavorableRadius(L2Fam=E1, neighbor=TotalVarNeighborhood(),
@@ -74,15 +74,15 @@
E1.x <- rexp(1e2, rate=(1-ind)*2+ind*10)
## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(E1.est0 <- ksEstimator(x=E1.x, Exp()))
+(E1.est0 <- MDEstimator(x=E1.x, ExpScaleFamily(), interval = c(0, 10)))
## 3. one-step estimation: radius known
-E1.Rob3 <- InfRobModel(center=ExpScaleFamily(rate=E1.est0$rate),
+E1.Rob3 <- InfRobModel(center=ExpScaleFamily(rate=1/E1.est0$estimate),
neighbor=ContNeighborhood(radius=0.5))
E1.IC9 <- optIC(model=E1.Rob3, risk=asMSE())
-(E1.est1 <- oneStepEstimator(E1.x, IC=E1.IC9, start=E1.est0$rate))
+(E1.est1 <- oneStepEstimator(E1.x, IC=E1.IC9, start=E1.est0$estimate))
## 4. one-step estimation: radius interval
-E1.IC10 <- radiusMinimaxIC(L2Fam=ExpScaleFamily(rate=E1.est0$rate),
+E1.IC10 <- radiusMinimaxIC(L2Fam=ExpScaleFamily(rate=1/E1.est0$estimate),
neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(E1.est2 <- oneStepEstimator(E1.x, IC=E1.IC10, start=E1.est0$rate))
+(E1.est2 <- oneStepEstimator(E1.x, IC=E1.IC10, start=E1.est0$estimate))
Modified: pkg/ROptEst/inst/scripts/GammaModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/GammaModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/GammaModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -4,16 +4,17 @@
require(ROptEst)
## generates Gamma Family with
-## scale = 1 and shape = 1
-G <- GammaFamily(scale = 1, shape = 2)
+## scale = 2 and shape = 0.5
+G <- GammaFamily(scale = 2, shape = 0.5)
G # show G
-plot(G) # plot of Gammad(scale = 1, shape = 2) and L_2 derivative
+plot(G) # plot of Gammad(scale = 2, shape = 0.5) and L_2 derivative
+distrExOptions(ErelativeTolerance = 1e-8) # increase precision for E
checkL2deriv(G)
## classical optimal IC
IC0 <- optIC(model = G, risk = asCov())
IC0 # show IC
-system.time(checkIC(IC0), gcFirst = TRUE)
+checkIC(IC0) # seems to be a numerical problem!?
Risks(IC0)
plot(IC0) # plot IC
@@ -22,25 +23,25 @@
RobG1 # show RobB1
## MSE solution
-system.time(IC1 <- optIC(model=RobG1, risk=asMSE()), gcFirst = TRUE)
+system.time(IC1 <- optIC(model=RobG1, risk=asMSE()))
IC1
checkIC(IC1)
Risks(IC1)
plot(IC1)
-x11()
+devNew()
infoPlot(IC1)
## lower case solutions
-system.time(IC2 <- optIC(model=RobG1, risk=asBias(), tol = 1e-10), gcFirst = TRUE)
+system.time(IC2 <- optIC(model=RobG1, risk=asBias(), tol = 1e-10))
IC2
checkIC(IC2)
Risks(IC2)
plot(IC2)
-x11()
+devNew()
infoPlot(IC2)
## Hampel solution
-system.time(IC3 <- optIC(model=RobG1, risk=asHampel(bound=clip(IC1))), gcFirst = TRUE)
+system.time(IC3 <- optIC(model=RobG1, risk=asHampel(bound=clip(IC1))))
IC3
checkIC(IC3)
Risks(IC3)
@@ -51,23 +52,26 @@
## radius minimax IC
## takes quite some time - about 30 min.
system.time(IC4 <- radiusMinimaxIC(L2Fam=G, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=Inf), gcFirst = TRUE)
+ risk=asMSE(), loRad=0, upRad=Inf))
## least favorable radius
-## takes quite some time - several hours!
+## takes really long time - several hours!
#system.time(r.rho1 <- leastFavorableRadius(L2Fam=G, neighbor=ContNeighborhood(),
# risk=asMSE(), rho=0.5))
## one-step estimation
## 1. generate a contaminated sample
ind <- rbinom(100, size=1, prob=0.05)
-x <- (1-ind)*rgamma(100, scale = 1, shape = 2) + ind*10
+x <- (1-ind)*rgamma(100, scale = 1, shape = 2) + ind*rgamma(100, scale = 3, shape = 5)
## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=x, Gammad()))
+(est0 <- MDEstimator(x=x, GammaFamily()))
+## non-robust ML estimator
+MLEstimator(x=x, GammaFamily())
+
## 3. one-step estimation: radius known
-RobG3 <- InfRobModel(center=GammaFamily(scale = est0$scale, shape = est0$shape),
+RobG3 <- InfRobModel(center=GammaFamily(scale = est0$estimate[1], shape = est0$estimate[2]),
neighbor=ContNeighborhood(radius=0.5))
IC9 <- optIC(model=RobG3, risk=asMSE())
-(est1 <- oneStepEstimator(x, IC=IC9, start=est0))
+(est1 <- oneStepEstimator(x, IC=IC9, start=est0$estimate))
Modified: pkg/ROptEst/inst/scripts/GumbelLocationModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/GumbelLocationModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/GumbelLocationModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -7,25 +7,24 @@
## generates Gumbel Location Family with loc = 0
## (known scale = 1)
-distrExOptions(ElowerTruncQuantile, 1e-15) # non-finite function value in integrate
-G0 <- GumbelLocationFamily(loc=0, scale=1)
+distrExOptions(ElowerTruncQuantile = 1e-15) # non-finite function value in integrate
+G0 <- GumbelLocationFamily(loc = 0, scale = 1)
G0 # show G0
plot(G0) # plot of Gumbel(loc = 0, scale = 1) and L_2 derivative
checkL2deriv(G0)
-# classical optimal IC
+## classical optimal IC
G0.IC0 <- optIC(model = G0, risk = asCov())
G0.IC0 # show IC
plot(G0.IC0) # plot IC
checkIC(G0.IC0)
-Risks(G0.IC0)
-# L_2 family + infinitesimal neighborhood
+## L_2 family + infinitesimal neighborhood
G0.Rob1 <- InfRobModel(center = G0, neighbor = ContNeighborhood(radius = 0.5))
G0.Rob1 # show G0.Rob1
G0.Rob2 <- InfRobModel(center = G0, neighbor = TotalVarNeighborhood(radius = 0.5))
-# MSE solution
+## MSE solution
E1.Rob1 <- InfRobModel(center = ExpScaleFamily(), neighbor = ContNeighborhood(radius = 0.5))
(E1.IC1 <- optIC(model=E1.Rob1, risk=asMSE()))
G0.IC1 <- optIC(model=G0.Rob1, risk=asMSE())
@@ -37,7 +36,8 @@
clip(G0.IC1)
cent(G0.IC1)
stand(G0.IC1)
-# alternatively
+
+## alternatively
G0.IC11 <- E1.IC1 # rate = 1!
CallL2Fam(G0.IC11) <- call("GumbelLocationFamily")
cent(G0.IC11) <- -cent(E1.IC1)
@@ -47,7 +47,6 @@
E1.Rob2 <- InfRobModel(center = ExpScaleFamily(), neighbor = TotalVarNeighborhood(radius = 0.5))
E1.IC2 <- optIC(model=E1.Rob2, risk=asMSE())
-#distrExOptions(ElowerTruncQuantile, 1e-15)
G0.IC2 <- optIC(model=G0.Rob2, risk=asMSE())
checkIC(G0.IC2)
Risks(G0.IC2)
@@ -57,7 +56,7 @@
clipLo(G0.IC2)
clipUp(G0.IC2)
stand(G0.IC2)
-# alternatively
+## alternatively
G0.IC21 <- E1.IC2 # rate = 1!
CallL2Fam(G0.IC21) <- call("GumbelLocationFamily")
clipLo(G0.IC21) <- -clipUp(E1.IC2)
@@ -66,7 +65,7 @@
checkIC(G0.IC21)
Risks(G0.IC21)
-# lower case solutions
+## lower case solutions
(G0.IC3 <- optIC(model=G0.Rob1, risk=asBias()))
checkIC(G0.IC3)
Risks(G0.IC3)
@@ -74,7 +73,7 @@
checkIC(G0.IC4)
Risks(G0.IC4)
-# Hampel solution
+## Hampel solution
(G0.IC5 <- optIC(model=G0.Rob1, risk=asHampel(bound=clip(G0.IC1))))
checkIC(G0.IC5)
Risks(G0.IC5)
@@ -82,25 +81,25 @@
checkIC(G0.IC6)
Risks(G0.IC6)
-# radius minimax IC
-# numerically instable for small 'loRad'!
-# => use connection to ExpScaleFamily for computations
-#(G0.IC7 <- radiusMinimaxIC(L2Fam=G0, neighbor=ContNeighborhood(),
-# risk=asMSE(), loRad=0.5, upRad=1.0))
-#checkIC(G0.IC7)
-#Risks(G0.IC7)
-#(G0.IC8 <- radiusMinimaxIC(L2Fam=G0, neighbor=TotalVarNeighborhood(),
-# risk=asMSE(), loRad=0.5, upRad=1.0))
-#checkIC(G0.IC8)
-#Risks(G0.IC8)
+## radius minimax IC
+## numerically instable for small 'loRad'!
+## => use connection to ExpScaleFamily for computations
+(G0.IC7 <- radiusMinimaxIC(L2Fam=G0, neighbor=ContNeighborhood(),
+ risk=asMSE(), loRad=0.5, upRad=1.0))
+checkIC(G0.IC7)
+Risks(G0.IC7)
+(G0.IC8 <- radiusMinimaxIC(L2Fam=G0, neighbor=TotalVarNeighborhood(),
+ risk=asMSE(), loRad=0.5, upRad=1.0))
+checkIC(G0.IC8)
+Risks(G0.IC8)
-# least favorable radius
-# numerically instable!
-# => use connection to ExpScaleFamily for computations
-#(G0.r.rho1 <- leastFavorableRadius(L2Fam=G0, neighbor=ContNeighborhood(),
-# risk=asMSE(), rho=0.5))
-#(G0.r.rho2 <- leastFavorableRadius(L2Fam=G0, neighbor=TotalVarNeighborhood(),
-# risk=asMSE(), rho=1/3))
+## least favorable radius
+## numerically instable!
+## => use connection to ExpScaleFamily for computations
+(G0.r.rho1 <- leastFavorableRadius(L2Fam=G0, neighbor=ContNeighborhood(),
+ risk=asMSE(), rho=0.5))
+(G0.r.rho2 <- leastFavorableRadius(L2Fam=G0, neighbor=TotalVarNeighborhood(),
+ risk=asMSE(), rho=1/3))
## one-step estimation
## 1. generate a contaminated sample
@@ -108,24 +107,24 @@
G0.x <- rgumbel(1e2, loc=(1-ind)*0.5+ind*1)
## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(G0.est0 <- ksEstimator(x=G0.x, Gumbel(), param = "loc"))
+(G0.est0 <- MDEstimator(x=G0.x, GumbelLocationFamily(), interval = c(0, 5)))
## 3. one-step estimation: radius known
-G0.Rob3 <- InfRobModel(center=GumbelLocationFamily(loc=G0.est0$loc),
- neighbor=ContNeighborhood(radius=0.5))
+G0.Rob3 <- InfRobModel(center=GumbelLocationFamily(loc=G0.est0$estimate),
+ neighbor=ContNeighborhood(radius=0.5))
G0.IC9 <- optIC(model=G0.Rob3, risk=asMSE())
-(G0.est1 <- oneStepEstimator(G0.x, IC=G0.IC9, start=G0.est0$loc))
+(G0.est1 <- oneStepEstimator(G0.x, IC=G0.IC9, start=G0.est0$estimate))
## 4. M estimation: radius known
G0.Rob31 <- InfRobModel(center=GumbelLocationFamily(loc=0),
- neighbor=ContNeighborhood(radius=0.5))
+ neighbor=ContNeighborhood(radius=0.5))
G0.IC91 <- optIC(model=G0.Rob31, risk=asMSE())
(G0.est11 <- locMEstimator(G0.x, IC=G0.IC91))
## 5. one-step estimation: radius interval
-G0.IC10 <- radiusMinimaxIC(L2Fam=GumbelLocationFamily(loc=G0.est0$loc),
+G0.IC10 <- radiusMinimaxIC(L2Fam=GumbelLocationFamily(loc=G0.est0$estimate),
neighbor=ContNeighborhood(), risk=asMSE(), loRad=0.5, upRad=1)
-(G0.est2 <- oneStepEstimator(G0.x, IC=G0.IC10, start=G0.est0$loc))
+(G0.est2 <- oneStepEstimator(G0.x, IC=G0.IC10, start=G0.est0$estimate))
## 6. M estimation: radius interval
G0.IC101 <- radiusMinimaxIC(L2Fam=GumbelLocationFamily(),
Modified: pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -16,7 +16,7 @@
checkL2deriv(N0)
-# classical optimal IC
+## classical optimal IC
LN1.IC0 <- optIC(model = LN1, risk = asCov())
LN1.IC0 # show IC
plot(LN1.IC0) # plot IC
@@ -29,7 +29,7 @@
Risks(N0.IC0)
-# L_2 family + infinitesimal neighborhood
+## L_2 family + infinitesimal neighborhood
LN1.Rob1 <- InfRobModel(center = LN1, neighbor = ContNeighborhood(radius = 0.5))
LN1.Rob1 # show LN1.Rob1
LN1.Rob2 <- InfRobModel(center = LN1, neighbor = TotalVarNeighborhood(radius = 0.25))
@@ -38,7 +38,7 @@
N0.Rob2 <- InfRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = 0.25))
-# MSE solution
+## MSE solution
LN1.IC1 <- optIC(model=LN1.Rob1, risk=asMSE())
checkIC(LN1.IC1)
Risks(LN1.IC1)
@@ -74,7 +74,7 @@
stand(N0.IC2)
-# lower case solutions
+## lower case solutions
LN1.IC3 <- optIC(model=LN1.Rob1, risk=asBias())
checkIC(LN1.IC3)
Risks(LN1.IC3)
@@ -96,7 +96,7 @@
plot(N0.IC4)
-# Hampel solution
+## Hampel solution
LN1.IC5 <- optIC(model=LN1.Rob1, risk=asHampel(bound=clip(LN1.IC1)))
checkIC(LN1.IC5)
Risks(LN1.IC5)
@@ -117,7 +117,7 @@
Risks(N0.IC6)
plot(N0.IC6)
-# radius minimax IC
+## radius minimax IC
(LN1.IC7 <- radiusMinimaxIC(L2Fam=LN1, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=0.5))
checkIC(LN1.IC7)
@@ -125,7 +125,7 @@
plot(LN1.IC7)
(N0.IC7 <- radiusMinimaxIC(L2Fam=N0, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0.1, upRad=0.5))
+ risk=asMSE(), loRad=0, upRad=0.5))
checkIC(N0.IC7)
Risks(N0.IC7)
plot(N0.IC7)
@@ -143,8 +143,8 @@
plot(N0.IC8)
-# least favorable radius
-# (may take quite some time!)
+## least favorable radius
+## (may take quite some time!)
(LN1.r.rho1 <- leastFavorableRadius(L2Fam=LN1, neighbor=ContNeighborhood(),
risk=asMSE(), rho=0.5))
(N0.r.rho1 <- leastFavorableRadius(L2Fam=N0, neighbor=ContNeighborhood(),
Modified: pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -9,38 +9,40 @@
plot(N0) # plot of Norm(mean = 0, sd = 1) and L_2 derivative
checkL2deriv(N0)
-# classical optimal IC
+## classical optimal IC
N0.IC0 <- optIC(model = N0, risk = asCov())
N0.IC0 # show IC
checkIC(N0.IC0)
Risks(N0.IC0)
plot(N0.IC0) # plot IC
-# L_2 family + infinitesimal neighborhood
+## L_2 family + infinitesimal neighborhood
N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 0.5))
N0.Rob1 # show N0.Rob1
-# MSE solution
-system.time(N0.IC1 <- optIC(model = N0.Rob1, risk = asMSE()), gcFirst = TRUE)
+## MSE solution
+system.time(N0.IC1 <- optIC(model = N0.Rob1, risk = asMSE()))
checkIC(N0.IC1)
Risks(N0.IC1)
plot(N0.IC1)
infoPlot(N0.IC1)
-system.time(N0.IC1.i <- optIC(model = N0.Rob1, risk = asMSE(normtype=InfoNorm())), gcFirst = TRUE)
+system.time(N0.IC1.i <- optIC(model = N0.Rob1, risk = asMSE(normtype=InfoNorm())))
checkIC(N0.IC1.i)
Risks(N0.IC1.i)
plot(N0.IC1.i)
infoPlot(N0.IC1.i)
-system.time(N0.IC1.s <- optIC(model = N0.Rob1, risk = asMSE(normtype=SelfNorm())), gcFirst = TRUE)
+system.time(N0.IC1.s <- optIC(model = N0.Rob1, risk = asMSE(normtype=SelfNorm())))
checkIC(N0.IC1.s)
Risks(N0.IC1.s)
plot(N0.IC1.s)
infoPlot(N0.IC1.s)
comparePlot(N0.IC1,N0.IC1.i,N0.IC1.s)
-# lower case solutions
+
+
+## lower case solutions
(N0.IC2 <- optIC(model = N0.Rob1, risk = asBias(), tol = 1e-10))
checkIC(N0.IC2)
Risks(N0.IC2)
@@ -53,15 +55,15 @@
plot(N0.IC2.i)
infoPlot(N0.IC2.i)
-# Hampel solution
+## Hampel solution
(N0.IC3 <- optIC(model = N0.Rob1, risk = asHampel(bound = clip(N0.IC1))))
checkIC(N0.IC3)
Risks(N0.IC3)
plot(N0.IC3)
infoPlot(N0.IC3)
-# radius minimax IC
-# (may take quite some time!)
+## radius minimax IC
+## (may take quite some time!)
(N0.IC4 <- radiusMinimaxIC(L2Fam=N0, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=Inf))
checkIC(N0.IC4)
@@ -85,10 +87,10 @@
infoPlot(N0.IC4.s)
-# least favorable radius
-# (may take quite some time!)
-#N0.r.rho1 <- leastFavorableRadius(L2Fam=N0, neighbor=ContNeighborhood(),
-# risk=asMSE(), rho=0.5)
+## least favorable radius
+## (may take quite some time!)
+N0.r.rho1 <- leastFavorableRadius(L2Fam=N0, neighbor=ContNeighborhood(),
+ risk=asMSE(), rho=0.5)
## one-step estimation
## 1. generate a contaminated sample
@@ -96,13 +98,13 @@
x <- rnorm(100, mean=0, sd=(1-ind) + ind*9)
## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- MDEstimator(x=x, NormLocationScaleFamily(), distance = KolmogorovDist))
+(est0 <- MDEstimator(x=x, NormLocationScaleFamily()))
## 3. one-step estimation: radius known
-N1 <- NormLocationScaleFamily(mean=est0$mean, sd=est0$sd)
+N1 <- NormLocationScaleFamily(mean=est0$estimate[1], sd=est0$estimate[2])
N1.Rob <- InfRobModel(center = N1, neighbor = ContNeighborhood(radius = 0.5))
IC1 <- optIC(model = N1.Rob, risk = asMSE())
-(est1 <- oneStepEstimator(x, IC1, est0))
+(est1 <- oneStepEstimator(x, IC1, est0$estimate))
## 4. one-step estimation: radius unknown
## rough estimate: 1-10% contamination
@@ -111,4 +113,4 @@
## takes some time
IC2 <- radiusMinimaxIC(L2Fam=N1, neighbor=ContNeighborhood(),risk=asMSE(),
loRad=0.1, upRad=1.0)
-(est2 <- oneStepEstimator(x, IC2, est0))
+(est2 <- oneStepEstimator(x, IC2, est0$estimate))
Modified: pkg/ROptEst/inst/scripts/NormalScaleModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/NormalScaleModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/NormalScaleModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -3,25 +3,25 @@
###############################################################################
require(ROptEst)
-## generates Normal Scale Family with scale = 1
-N0 <- NormScaleFamily(mean=0, sd=1)
+## generates Normal Scale Family with scale = 3
+N0 <- NormScaleFamily(mean=2, sd=3)
N0 # show G0
-plot(N0) # plot of Norm(mean = 0, sd = 1) and L_2 derivative
+plot(N0) # plot of Norm(mean = 2, sd = 3) and L_2 derivative
checkL2deriv(N0)
-# classical optimal IC
+## classical optimal IC
N0.IC0 <- optIC(model = N0, risk = asCov())
N0.IC0 # show IC
plot(N0.IC0) # plot IC
checkIC(N0.IC0)
Risks(N0.IC0)
-# L_2 family + infinitesimal neighborhood
+## L_2 family + infinitesimal neighborhood
N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 0.5))
N0.Rob1 # show N0.Rob1
N0.Rob2 <- InfRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = 0.5))
-# MSE solution
+## MSE solution
(N0.IC1 <- optIC(model=N0.Rob1, risk=asMSE()))
checkIC(N0.IC1)
Risks(N0.IC1)
@@ -32,7 +32,7 @@
Risks(N0.IC2)
plot(N0.IC2)
-# lower case solutions
+## lower case solutions
(N0.IC3 <- optIC(model=N0.Rob1, risk=asBias()))
checkIC(N0.IC3)
Risks(N0.IC3)
@@ -42,7 +42,7 @@
Risks(N0.IC4)
plot(N0.IC4)
-# Hampel solution
+## Hampel solution
(N0.IC5 <- optIC(model=N0.Rob1, risk=asHampel(bound=clip(N0.IC1))))
checkIC(N0.IC5)
Risks(N0.IC5)
@@ -52,7 +52,7 @@
Risks(N0.IC6)
plot(N0.IC6)
-# radius minimax IC
+## radius minimax IC
(N0.IC7 <- radiusMinimaxIC(L2Fam=N0, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=Inf))
checkIC(N0.IC7)
@@ -64,8 +64,8 @@
Risks(N0.IC8)
plot(N0.IC8)
-# least favorable radius
-# (may take quite some time!)
+## least favorable radius
+## (may take quite some time!)
(N0.r.rho1 <- leastFavorableRadius(L2Fam=N0, neighbor=ContNeighborhood(),
risk=asMSE(), rho=0.5))
(N0.r.rho2 <- leastFavorableRadius(L2Fam=N0, neighbor=TotalVarNeighborhood(),
Modified: pkg/ROptEst/inst/scripts/PoissonModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/PoissonModel.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/PoissonModel.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -12,14 +12,14 @@
plot(P) # plot of Pois(lambda = 4.5) and L_2 derivative
checkL2deriv(P)
-# classical optimal IC
+## classical optimal IC
IC0 <- optIC(model = P, risk = asCov())
IC0 # show IC
checkIC(IC0)
Risks(IC0)
plot(IC0) # plot IC
-# L_2 family + infinitesimal neighborhood
+## L_2 family + infinitesimal neighborhood
RobP1 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 0.5))
RobP1 # show RobP1
(RobP2 <- InfRobModel(center = P, neighbor = TotalVarNeighborhood(radius = 0.5)))
@@ -28,7 +28,7 @@
lowerCaseRadius(L2Fam = P, ContNeighborhood(radius = 0.5), risk = asMSE())
lowerCaseRadius(L2Fam = P, TotalVarNeighborhood(radius = 0.5), risk = asMSE())
-# MSE solution
+## MSE solution
(IC1 <- optIC(model=RobP1, risk=asMSE()))
checkIC(IC1)
Risks(IC1)
@@ -50,14 +50,14 @@
plot(IC2)
-# lower case solutions
+## lower case solutions
(IC3 <- optIC(model=RobP1, risk=asBias()))
checkIC(IC3)
Risks(IC3)
plot(IC3)
(IC3.p <- optIC(model=RobP1, risk=asBias(biastype=positiveBias())))
-checkIC(IC3.p)
+checkIC(IC3.p) # numerical problem???
Risks(IC3.p)
plot(IC3.p)
@@ -72,7 +72,7 @@
Risks(IC4)
plot(IC4)
-# Hampel solution
+## Hampel solution
(IC5 <- optIC(model=RobP1, risk=asHampel(bound=clip(IC1))))
checkIC(IC5)
Risks(IC5)
@@ -84,7 +84,7 @@
plot(IC6)
-# radius minimax IC
+## radius minimax IC
(IC7 <- radiusMinimaxIC(L2Fam=P, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=0.5))
checkIC(IC7)
@@ -109,8 +109,8 @@
Risks(IC8)
plot(IC8)
-# least favorable radius
-# (may take quite some time!)
+## least favorable radius
+## (may take quite some time!)
(r.rho1 <- leastFavorableRadius(L2Fam=P, neighbor=ContNeighborhood(),
risk=asMSE(), rho=0.5))
(r.rho2 <- leastFavorableRadius(L2Fam=P, neighbor=TotalVarNeighborhood(),
@@ -122,21 +122,21 @@
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))
-
+
## 0. mean (classical optimal)
(est0 <- mean(x))
## 1. Kolmogorov(-Smirnov) minimum distance estimator
-(est1 <- MDEstimator(x=x, PoisFamily(), distance = KolmogorovDist))
+(est1 <- MDEstimator(x=x, PoisFamily(), interval = c(0, 10)))
## 2. one-step estimation: radius interval
## 2.1 small amount of contamination < 2%
-IC9 <- radiusMinimaxIC(L2Fam=PoisFamily(lambda=est1$lambda),
+IC9 <- radiusMinimaxIC(L2Fam=PoisFamily(lambda=est1$estimate),
neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=1)
-(est21 <- oneStepEstimator(x, IC=IC9, start=est1$lambda))
+(est21 <- oneStepEstimator(x, IC=IC9, start=est1$estimate))
## 2.2 amount of contamination unknown
-IC10 <- radiusMinimaxIC(L2Fam=PoisFamily(lambda=est1$lambda),
+IC10 <- radiusMinimaxIC(L2Fam=PoisFamily(lambda=est1$estimate),
neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(est22 <- oneStepEstimator(x, IC=IC10, start=est1$lambda))
+(est22 <- oneStepEstimator(x, IC=IC10, start=est1$estimate))
distroptions("TruncQuantile", 1e-5) # default
Modified: pkg/ROptEst/inst/scripts/UnderOverShootRisk.R
===================================================================
--- pkg/ROptEst/inst/scripts/UnderOverShootRisk.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptEst/inst/scripts/UnderOverShootRisk.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -12,10 +12,10 @@
N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 0))
N0.Rob2 <- InfRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = 0))
-system.time(IC0c <- optIC(model=N0.Rob1, risk=asUnOvShoot(width = tau)), gcFirst = TRUE)
+system.time(IC0c <- optIC(model=N0.Rob1, risk=asUnOvShoot(width = tau)))
checkIC(IC0c)
Risks(IC0c)
-system.time(IC0v <- optIC(model=N0.Rob2, risk=asUnOvShoot(width = tau)), gcFirst = TRUE)
+system.time(IC0v <- optIC(model=N0.Rob2, risk=asUnOvShoot(width = tau)))
checkIC(IC0v)
Risks(IC0v)
@@ -23,22 +23,22 @@
N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 2*tau*1/sqrt(2*pi)))
N0.Rob2 <- InfRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = tau*1/sqrt(2*pi)))
-system.time(IC0c <- optIC(model=N0.Rob1, risk=asUnOvShoot(width = tau)), gcFirst = TRUE)
+system.time(IC0c <- optIC(model=N0.Rob1, risk=asUnOvShoot(width = tau)))
checkIC(IC0c)
Risks(IC0c)
-system.time(IC0v <- optIC(model=N0.Rob2, risk=asUnOvShoot(width = tau)), gcFirst = TRUE)
+system.time(IC0v <- optIC(model=N0.Rob2, risk=asUnOvShoot(width = tau)))
checkIC(IC0v)
Risks(IC0v)
-# L_2 family + infinitesimal resp. fixed neighborhood
+## L_2 family + infinitesimal resp. fixed neighborhood
rad <- 0.5
N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = rad))
N0.Rob2 <- InfRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = rad/2))
N0.Rob3 <- FixRobModel(center = N0, neighbor = ContNeighborhood(radius = rad/sqrt(n)))
N0.Rob4 <- FixRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = rad/2/sqrt(n)))
-# asUnOvShoot solution
+## asUnOvShoot solution
N0.IC1 <- optIC(model = N0.Rob1, risk = asUnOvShoot(width = tau))
checkIC(N0.IC1)
Risks(N0.IC1)
@@ -49,7 +49,7 @@
Risks(N0.IC2)
plot(N0.IC2)
-# fiUnOvShoot solution
+## fiUnOvShoot solution
N0.IC3 <- optIC(model=N0.Rob3, risk=fiUnOvShoot(width = tau/sqrt(n)), sampleSize = n)
checkIC(N0.IC3)
Risks(N0.IC3)
@@ -60,8 +60,8 @@
Risks(N0.IC4)
plot(N0.IC4)
-# O(n^(-0.5))-corrected solution
-# in case of contamination neighborhoods
+## O(n^(-0.5))-corrected solution
+## in case of contamination neighborhoods
N0.IC5 <- N0.IC1
clipUp1 <- clipUp(N0.IC1)/as.vector(stand(N0.IC1))
clipUp5 <- max(0, clipUp1 - rad*(rad + clipUp1*tau)/(sqrt(n)*2*tau*pnorm(-clipUp1)))
@@ -75,8 +75,8 @@
getRiskIC(N0.IC5, asUnOvShoot(width = tau), ContNeighborhood(radius=rad))
getRiskIC(N0.IC5, fiUnOvShoot(width = tau/sqrt(n)), ContNeighborhood(radius=rad/sqrt(n)), sampleSize = n)
-# O(n^(-1))-corrected solution
-# in case of total variation neighborhoods
+## O(n^(-1))-corrected solution
+## in case of total variation neighborhoods
N0.IC6 <- N0.IC2
clipUp2 <- clipUp(N0.IC2)/as.vector(stand(N0.IC2))
clipUp6 <- max(0, clipUp2 - tau*(2*clipUp2^2*rad/2 + tau*dnorm(clipUp2))/(6*n*pnorm(-clipUp2)))
@@ -99,25 +99,25 @@
## 2. M estimation
N0.Rob5 <- InfRobModel(center = NormLocationFamily(mean = 0),
- neighbor = ContNeighborhood(radius = 0.5))
+ neighbor = ContNeighborhood(radius = 0.5))
N0.IC7 <- optIC(model=N0.Rob5, risk=asUnOvShoot(width = 1.960))
(Mest1 <- locMEstimator(X, IC=N0.IC7))
N0.Rob6 <- FixRobModel(center = NormLocationFamily(mean = 0),
- neighbor = ContNeighborhood(radius = 0.05))
-N0.IC8 <- optIC(model = N0.Rob6, risk=fiUnOvShoot(width = 0.196), sampleSize = 1e2)
+ neighbor = ContNeighborhood(radius = 0.05))
+N0.IC8 <- optIC(model = N0.Rob6, risk=fiUnOvShoot(width = 1.960/sqrt(n)), sampleSize = 1e2)
(Mest2 <- locMEstimator(X, IC=N0.IC8))
## 3. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=X, Norm(), param = "mean"))
+(est0 <- MDEstimator(x=X, NormLocationFamily(), interval = c(-5, 5)))
## 4. one-step estimation
-N0.Rob7 <- InfRobModel(center = NormLocationFamily(mean = est0$mean),
- neighbor = ContNeighborhood(radius=0.5))
+N0.Rob7 <- InfRobModel(center = NormLocationFamily(mean = est0$estimate),
+ neighbor = ContNeighborhood(radius=0.5))
N0.IC9 <- optIC(model=N0.Rob7, risk=asUnOvShoot(width = 1.960))
-(est1 <- oneStepEstimator(X, IC = N0.IC9, start = est0$mean))
-N0.Rob8 <- FixRobModel(center = NormLocationFamily(mean = est0$mean),
- neighbor = ContNeighborhood(radius=0.05))
-N0.IC10 <- optIC(model=N0.Rob8, risk=fiUnOvShoot(width = 0.196), sampleSize = 1e2)
-(est2 <- oneStepEstimator(X, IC = N0.IC10, start = est0$mean))
+(est1 <- oneStepEstimator(X, IC = N0.IC9, start = est0$estimate))
+N0.Rob8 <- FixRobModel(center = NormLocationFamily(mean = est0$estimate),
+ neighbor = ContNeighborhood(radius=0.05))
+N0.IC10 <- optIC(model=N0.Rob8, risk=fiUnOvShoot(width = 1.960/sqrt(n)), sampleSize = 1e2)
+(est2 <- oneStepEstimator(X, IC = N0.IC10, start = est0$estimate))
Modified: pkg/ROptRegTS/DESCRIPTION
===================================================================
--- pkg/ROptRegTS/DESCRIPTION 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/ROptRegTS/DESCRIPTION 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,14 +1,13 @@
Package: ROptRegTS
-Version: 0.5.0
-Date: 2007-07-30
+Version: 0.6.0
+Date: 2008-07-21
Title: Optimally robust estimation for regression-type models
Description: Optimally robust estimation for regression-type
models using S4 classes and methods
Depends: R (>= 2.4.0), methods, distr(>= 1.9), distrEx(>= 1.9), RandVar(>= 0.6), ROptEst(>= 0.5.0)
-Author: Matthias Kohl <Matthias.Kohl at stamats.de>
+Author: Matthias Kohl <Matthias.Kohl at stamats.de>, Peter Ruckdeschel
Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
-SaveImage: no
LazyLoad: yes
License: GPL version 2 or later
-URL: http://www.stamats.de/RobASt.htm
-Packaged: Mon Aug 6 08:09:59 2007; kohl
+URL: http://robast.r-forge.r-project.org/
+Packaged: Mon Aug 6 08:09:59 2007; kohl
Modified: pkg/RandVar/DESCRIPTION
===================================================================
--- pkg/RandVar/DESCRIPTION 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/DESCRIPTION 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,6 +1,6 @@
Package: RandVar
Version: 0.6.3
-Date: 2008-04-23
+Date: 2008-07-21
Title: Implementation of random variables
Description: Implementation of random variables by means
of S4 classes and methods
@@ -9,5 +9,5 @@
Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
LazyLoad: yes
License: GPL version 2 or later
-URL: http://www.uni-bayreuth.de/departments/math/org/mathe7/DISTR/RandVar.html
+URL: http://robast.r-forge.r-project.org/
Packaged: Tue Oct 2 15:25:20 2007; Matthias Kohl
Modified: pkg/RandVar/R/EuclRandVariable.R
===================================================================
--- pkg/RandVar/R/EuclRandVariable.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/R/EuclRandVariable.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -230,7 +230,7 @@
setMethod("evalRandVar", signature(RandVar = "EuclRandMatrix",
x = "numeric",
distr = "missing"),
- function(x, RandVar){
+ function(RandVar, x){
if(!is.null(RandVar at Domain)){
if(length(x) != RandVar at Domain@dimension)
stop("x has wrong dimension")
@@ -271,7 +271,7 @@
setMethod("evalRandVar", signature(RandVar = "EuclRandMatrix",
x = "numeric",
distr = "Distribution"),
- function(x, RandVar, distr){
+ function(RandVar, x, distr){
if(is.null(RandVar at Domain))
stop("domain of 'RandVar' is 'NULL'")
if(ncol(x) != RandVar at Domain@dimension)
Deleted: pkg/RandVar/inst/doc/RandVar.aux
===================================================================
--- pkg/RandVar/inst/doc/RandVar.aux 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/inst/doc/RandVar.aux 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,42 +0,0 @@
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Deleted: pkg/RandVar/inst/doc/RandVar.brf
===================================================================
--- pkg/RandVar/inst/doc/RandVar.brf 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/inst/doc/RandVar.brf 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,8 +0,0 @@
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-\backcite {distr}{{9}{2}{table.1}}
-\backcite {MK:05}{{9}{3}{section.3}}
Deleted: pkg/RandVar/inst/doc/RandVar.log
===================================================================
--- pkg/RandVar/inst/doc/RandVar.log 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/inst/doc/RandVar.log 2008-07-24 17:07:22 UTC (rev 125)
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-er/m/n/10.95 , \T1/aer/bx/n/10.95 6\T1/aer/m/n/10.95 (2): 10--13. http://CRAN.
-R-
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Deleted: pkg/RandVar/inst/doc/RandVar.out
===================================================================
--- pkg/RandVar/inst/doc/RandVar.out 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/inst/doc/RandVar.out 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,3 +0,0 @@
-\BOOKMARK [1][-]{section.1}{S4 Classes}{}
-\BOOKMARK [1][-]{section.2}{Functions and Methods}{}
-\BOOKMARK [1][-]{section.3}{Odds and Ends}{}
Modified: pkg/RandVar/inst/doc/RandVar.pdf
===================================================================
(Binary files differ)
Deleted: pkg/RandVar/inst/doc/RandVar.tex
===================================================================
--- pkg/RandVar/inst/doc/RandVar.tex 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/inst/doc/RandVar.tex 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,466 +0,0 @@
-%\VignetteIndexEntry{RandVar}
-%\VignetteDepends{distr,distrEx}
-%\VignetteKeywords{random variable, S4 classes, S4 methods}
-%\VignettePackage{RandVar}
-%
-\documentclass[11pt]{article}
-\usepackage{geometry}\usepackage{color}
-\usepackage{ifpdf}
-\definecolor{darkblue}{rgb}{0.0,0.0,0.75}
-\usepackage[%
-baseurl={http://www.stamats.de},%
-pdftitle={RandVar: Implementation of random variables by means of S4 classes and methods},%
-pdfauthor={Matthias Kohl},%
-pdfsubject={RandVar},%
-pdfkeywords={random variable, S4 classes, S4 methods},%
-pagebackref,bookmarks,colorlinks,linkcolor=darkblue,citecolor=darkblue,%
-pagecolor=darkblue,raiselinks,plainpages,pdftex]{hyperref}
-%
-\markboth{\sl Packages ``{\tt RandVar}''}{\sl Packages ``{\tt RandVar}''}
-%
-% -------------------------------------------------------------------------------
-\newcommand{\code}[1]{{\tt #1}}
-\newcommand{\pkg}[1]{{\tt "#1"}}
-\newcommand{\pkgversion}{{\tt 2.0}}
-\newcommand{\pkgExversion}{{\tt 0.6.2}}
-% -------------------------------------------------------------------------------
-%
-% -------------------------------------------------------------------------------
-\usepackage{/home/btm722/RTOP/R-2-7-branch/share/texmf/Sweave}
-\begin{document}
-% -------------------------------------------------------------------------------
-\title{RandVar: Implementation of random variables by means
- of {\tt S4} classes and methods}
-\author{Matthias Kohl\\
-e-Mail: {\tt matthias.kohl at stamats.de}\\
-}
-\maketitle
-% -------------------------------------------------------------------------------
-\begin{abstract}
-% -------------------------------------------------------------------------------
-In this package we implement random variables by means of {\tt S4} classes
-and methods. This vignette corresponds to Appendix D.2 in Kohl~(2005)~\cite{MK:05}.
-% -------------------------------------------------------------------------------
-\end{abstract}
-% -------------------------------------------------------------------------------
-\tableofcontents
-% -------------------------------------------------------------------------------
-\clearpage
-% -----------------------------------------------------------------------
-\section{{\tt S4} Classes}
-% -----------------------------------------------------------------------
-The {\tt S4} class {\tt RandVariable} (cf.\ Figure~\ref{ap.Rpack.RandVar.dia1})
-has the slots {\tt Map}, {\tt Domain} and {\tt Range} where {\tt Map}
-contains a list of functions which are measurable maps from {\tt Domain}
-to {\tt Range}. The elements contained in the list {\tt Map} must be functions in
-one argument named {\tt x}. We do not allow further parameters for these
-functions as this would lead to inconsistent objects. Strictly speaking, an object of
-class {\tt RandVariable} would represent not only one random variable but a whole
-set of random variables depending on these parameters.
-% -----------------------------------------------------------------------
-\par
-The slots {\tt Domain} and {\tt Range} are filled with an object of
-class {\tt OptionalrSpace}; i.e., they contain {\tt NULL} or an object of class
-{\tt rSpace} (see package {\tt distr}~\cite{distr}). In case of
-{\tt EuclRandVariable} and {\tt RealRandVariable} the slot {\tt Range} is filled
-with an object of class {\tt Euclideanspace} and {\tt Reals}, respectively. The
-class {\tt EuclRandMatrix} additionally has the slot {\tt Dim} which is a vector
-of integers and contains the dimensions of the Euclidean random matrix.
-% -----------------------------------------------------------------------
-\par
-Using these {\tt S4} classes there are two possibilities to implement a ${\rm R}^k$
-valued random variable. First, we could define a {\tt EuclRandVariable}
-whose slot {\tt Map} contains a list with one function which maps to ${\rm R}^k$;
-i.e., the slot {\tt Range} is a $k$-dimensional Euclidean space. Second, we could
-define a {\tt EuclRandVariable} whose slot {\tt Map} contains a list with $n$
-functions (projections) which map to ${\rm R}^m$ where $k = m*n$. Now, the slot
-{\tt Range} is an $m$-dimensional Euclidean space.
-Since it is sometimes convenient to regard a ${\rm R}^k$ valued random variable
-as measurable map consisting of ${\rm R}^{k_i}$ valued maps where $\sum k_i = k$,
-we introduce a further class called {\tt EuclRandVarList}.
-With this class we can now define ${\rm R}^k$ valued random variables as a list
-of ${\rm R}^{k_i}$ valued random variables with compatible domains. More precisely,
-the elements of a {\tt EuclRandVarList} may even have very different ranges
-(not necessarily Euclidean spaces) they only need to have compatible
-domains which is checked via the generic function {\tt compatibleDomains}.
-% -----------------------------------------------------------------------
-\begin{figure}[!ht]
-\begin{center}
- \ifpdf
- \includegraphics[scale=1.0, viewport = 14 15 244 275]{RandVariable.pdf}
- \else
- \includegraphics[scale=1.0]{RandVariable.eps}
- \fi
- \caption[Class {\tt RandVariable} and Subclasses]{Class
- {\tt RandVariable} and subclasses.}
- \label{ap.Rpack.RandVar.dia1}
-\end{center}
-\end{figure}
-% -----------------------------------------------------------------------
-\newpage
-\section{Functions and Methods}
-% -----------------------------------------------------------------------
-As in case of package {\tt distrEx}~\cite{distr}, we follow the advices of
-Section~7.3 of \cite{Cham:98} and \cite{Gent:03}. That is, we introduce
-generating functions as well as accessor and replacement functions. A short
-description of the implemented generating functions is given in
-Table~\ref{ap.Rpack.RandVar.tab.gen}.
-% -----------------------------------------------------------------------
-\begin{table}[!ht]
-\begin{center}
-\begin{tabular}{p{4cm}|p{7.5cm}}
- \textbf{Generating Function} & \textbf{Description} \\ \hline\hline
- {\tt EuclRandMatrix} & Generates an object of class {\tt EuclRandMatrix}\\ \hline
- {\tt EuclRandVariable} & Generates an object of class {\tt EuclRandVariable}\\ \hline
- {\tt EuclRandVarList} & Generates an object of class {\tt EuclRandVarList}\\ \hline
- {\tt RandVariable} & Generates an object of class {\tt RandVariable}\\ \hline
- {\tt RealRandVariable} & Generates an object of class {\tt RealRandVariable}
-\end{tabular}
-\caption[Generating Functions of Package {\tt RandVar}]{Generating
-functions of package {\tt RandVar}.}\label{ap.Rpack.RandVar.tab.gen}%
-\end{center}
-\end{table}
-% -----------------------------------------------------------------------
-\par\noindent
-While there are accessor functions for all slots of the newly defined
-{\tt S4} classes, replacement functions are only implemented for those
-slots a user should modify.
-% -----------------------------------------------------------------------
-\par
-Our next goal was that one can use these classes of random variables like
-ordinary numeric vectors or matrices. Hence, we overloaded the {\tt S4}
-group generic functions {\tt Arith} and {\tt Math} as well as matrix
-multiplication {\tt \%*\%}. For the matrix multiplication of {\tt EuclRandVarList}s
-we additionally introduced the operator {\tt \%m\%}.
-Now, if we have random variables {\tt X} and {\tt Y}, a numerical
-vector {\tt v} and a numerical matrix {\tt M} (with compatible dimensions)
-we can for instance generate
-\begin{Schunk}
-\begin{Sinput}
-> library(RandVar)
-> (X <- RealRandVariable(Map = list(function(x) {
-+ x
-+ }, function(x) {
-+ x^2
-+ }), Domain = Reals(), Range = Reals()))
-\end{Sinput}
-\begin{Soutput}
-An object of class “RealRandVariable”
-length of Map: 2
-Domain: Real Space with dimension 1
-Range: Real Space with dimension 1
-\end{Soutput}
-\begin{Sinput}
-> Map(X)
-\end{Sinput}
-\begin{Soutput}
-[[1]]
-function (x)
-{
- x
-}
-
-[[2]]
-function (x)
-{
- x^2
-}
-\end{Soutput}
-\begin{Sinput}
-> evalRandVar(X, 2)
-\end{Sinput}
-\begin{Soutput}
- [,1]
-[1,] 2
-[2,] 4
-\end{Soutput}
-\begin{Sinput}
-> evalRandVar(X, as.matrix(seq(2, 10, 2)))
-\end{Sinput}
-\begin{Soutput}
-, , 1
-
- [,1] [,2] [,3] [,4] [,5]
-[1,] 2 4 6 8 10
-[2,] 4 16 36 64 100
-\end{Soutput}
-\begin{Sinput}
-> R1 <- exp(X - 1)
-> Map(R1)
-\end{Sinput}
-\begin{Soutput}
-[[1]]
-function (x)
-{
- f1 <- function (x)
- {
- f1 <- function (x)
- {
- x
- }
- f1(x) - 1
- }
- exp(f1(x))
-}
-<environment: 0x8cd175c>
-
-[[2]]
-function (x)
-{
- f1 <- function (x)
- {
- f1 <- function (x)
- {
- x^2
- }
- f1(x) - 1
- }
- exp(f1(x))
-}
-<environment: 0x8cd175c>
-\end{Soutput}
-\begin{Sinput}
-> R2 <- exp(X - 1:2)
-> Map(R2)
-\end{Sinput}
-\begin{Soutput}
-[[1]]
-function (x)
-{
- f1 <- function (x)
- {
- f1 <- function (x)
- {
- x
- }
- f1(x) - 1L
- }
- exp(f1(x))
-}
-<environment: 0x90d97e4>
-
-[[2]]
-function (x)
-{
- f1 <- function (x)
- {
- f1 <- function (x)
- {
- x^2
- }
- f1(x) - 2L
- }
- exp(f1(x))
-}
-<environment: 0x90d97e4>
-\end{Soutput}
-\begin{Sinput}
-> (Y <- RealRandVariable(Map = list(function(x) {
-+ sin(x)
-+ }, function(x) {
-+ cos(x)
-+ }), Domain = Reals(), Range = Reals()))
-\end{Sinput}
-\begin{Soutput}
-An object of class “RealRandVariable”
-length of Map: 2
-Domain: Real Space with dimension 1
-Range: Real Space with dimension 1
-\end{Soutput}
-\begin{Sinput}
-> Map(Y)
-\end{Sinput}
-\begin{Soutput}
-[[1]]
-function (x)
-{
- sin(x)
-}
-
-[[2]]
-function (x)
-{
- cos(x)
-}
-\end{Soutput}
-\begin{Sinput}
-> R3 <- X %*% Y
-> dimension(R3)
-\end{Sinput}
-\begin{Soutput}
-[1] 1
-\end{Soutput}
-\begin{Sinput}
-> 2 * sin(2) + 2^2 * cos(2)
-\end{Sinput}
-\begin{Soutput}
-[1] 0.1540075
-\end{Soutput}
-\begin{Sinput}
-> (R4 <- X %*% t(Y))
-\end{Sinput}
-\begin{Soutput}
-An object of class “EuclRandMatrix”
-Dim of Map: 2 2
-Domain: Real Space with dimension 1
-Range: Euclidean Space with dimension 1
-\end{Soutput}
-\begin{Sinput}
-> dimension(R4)
-\end{Sinput}
-\begin{Soutput}
-[1] 4
-\end{Soutput}
-\begin{Sinput}
-> (M <- matrix(c(2 * sin(2), 2^2 * sin(2), 2 * cos(2), 2^2 * cos(2)),
-+ ncol = 2))
-\end{Sinput}
-\begin{Soutput}
- [,1] [,2]
-[1,] 1.818595 -0.8322937
-[2,] 3.637190 -1.6645873
-\end{Soutput}
-\begin{Sinput}
-> (R5 <- M %*% R4)
-\end{Sinput}
-\begin{Soutput}
-An object of class “EuclRandMatrix”
-Dim of Map: 2 2
-Domain: Real Space with dimension 1
-Range: Real Space with dimension 1
-\end{Soutput}
-\end{Schunk}
-We also implemented {\tt S4} methods for the generic function {\tt E} of
-package {\tt distrEx}~\cite{distr}. That is, given some distribution {\tt D},
-respectively some conditional distribution {\tt CD} and some random variable {\tt X}
-we can compute the (conditional) expectation of {\tt X} under {\tt D}, respectively
-{\tt CD} simply by
-\begin{Schunk}
-\begin{Sinput}
-> D <- Norm()
-> E(object = D, fun = X)
-\end{Sinput}
-\begin{Soutput}
-[1] 2.587378e-16 9.999942e-01
-\end{Soutput}
-\begin{Sinput}
-> E(D)
-\end{Sinput}
-\begin{Soutput}
-[1] 0
-\end{Soutput}
-\begin{Sinput}
-> var(D)
-\end{Sinput}
-\begin{Soutput}
-[1] 1
-\end{Soutput}
-\begin{Sinput}
-> (CD <- LMCondDistribution(theta = 1))
-\end{Sinput}
-\begin{Soutput}
-Distribution object of class: AbscontCondDistribution
-theta : 1
-intercept : 0
-scale : 1
-## cond:
-name: conditioning by an Euclidean space
-Range: Euclidean Space with dimension 1
-\end{Soutput}
-\begin{Sinput}
-> E(object = CD, fun = X, cond = 2)
-\end{Sinput}
-\begin{Soutput}
-[1] 2.000000 4.999993
-\end{Soutput}
-\begin{Sinput}
-> E(Norm(mean = 2))
-\end{Sinput}
-\begin{Soutput}
-[1] 2
-\end{Soutput}
-\begin{Sinput}
-> E(Norm(mean = 2), fun = function(x) {
-+ x^2
-+ })
-\end{Sinput}
-\begin{Soutput}
-[1] 4.999993
-\end{Soutput}
-\end{Schunk}
-for some given condition {\tt cond}.
-% -----------------------------------------------------------------------
-\par
-In addition, we define methods for the generic function {\tt show} for the classes
-{\tt RandVariable}, {\tt EuclRandMatrix} and {\tt EuclRandVarList}. There are
-also methods for the generic functions {\tt dimension} (see package
-{\tt distr}~\cite{distr}), {\tt length}, {\tt ncol}, {\tt nrow}, {\tt t} and
-{\tt [} (cf.\ package {\tt base}). For more details we refer to the corresponding
-help pages.
-% -----------------------------------------------------------------------
-\par
-Finally, we introduce several new generic functions. A brief description
-of these functions is given in Table~\ref{ap.Rpack.RandVar.tab}.
-% -----------------------------------------------------------------------
-\begin{table}[!ht]
-\begin{center}
-\begin{tabular}{p{3.5cm}|p{8cm}}
- \textbf{Generic Function} & \textbf{Description} \\ \hline\hline
- \texttt{\%m\%} & matrix multiplication for {\tt EuclRandVarList}s \\ \hline
- \texttt{compatibleDomains} & test if the domains of two random variables
- are compatible \\ \hline
- \texttt{evalRandVar} & evaluation of random variables\\ \hline
- \texttt{imageDistr} & image distribution of some distribution under some
- random variable \\ \hline
- \texttt{numberOfMaps} & number of functions contained in the slots {\tt Map}
- of the members of a {\tt EuclRandVarList}
-\end{tabular}
-\caption[New Generic Functions of Package {\tt RandVar}]
-{New generic functions of package {\tt RandVar} (without accessor
-and replacement functions).}\label{ap.Rpack.RandVar.tab}%
-\end{center}
-\end{table}
-% -----------------------------------------------------------------------
-\par\noindent
-For more details about the full functionality of package {\tt RandVar}
-we refer to the source code and the corresponding help pages, respectively.
-% -----------------------------------------------------------------------
-\section{Odds and Ends}
-% -----------------------------------------------------------------------
-The main issue is to reduce the computation time for methods using
-objects of class {\tt RandVariable} and its subclasses as these classes
-play an important role in the computation of optimally robust
-estimators; confer Kohl~(2005)~\cite{MK:05}.
-In particular, we are looking for ways to increase the computation speed
-of {\tt evalRandVar} and {\tt E}.
-%-------------------------------------------------------------------------------
-\begin{thebibliography}{8}
-
-\bibitem{Cham:98}
-Chambers J.M.
-\newblock {\em {Programming with data. A guide to the S language}\/}.
-\newblock {Springer}.
-\newblock http://cm.bell-labs.com/stat/Sbook/index.html
-
-\bibitem{Gent:03}
-Gentleman R.
-\newblock {\em Object Orientated Programming. Slides of a Short Course held in Auckland\/}.
-\newblock http://www.stat.auckland.ac.nz/S-Workshop/Gentleman/Methods.pdf
-
-\bibitem{MK:05}
-Kohl M.
-\newblock {\em Numerical Contributions to the Asymptotic Theory of Robustness\/}.
-\newblock {Dissertation}, Universit\"at Bayreuth.
-\newblock See also http://stamats.de/ThesisMKohl.pdf
-
-\bibitem{distr}
-Ruckdeschel P., Kohl M., Stabla T., and Camphausen F.
-\newblock {S4 Classes for Distributions.}
-\newblock {\em R-News\/}, {\bf 6}(2): 10--13.
-\newblock http://CRAN.R-project.org/doc/Rnews/Rnews\_2006-2.pdf
-\newblock See also {http://www.uni-bayreuth.de/departments/math/org/mathe7/RUCKDESCHEL/pubs/distr.pdf}
-
-\end{thebibliography}
-% -------------------------------------------------------------------------------
-\end{document}
-% -------------------------------------------------------------------------------
Deleted: pkg/RandVar/inst/doc/RandVar.toc
===================================================================
--- pkg/RandVar/inst/doc/RandVar.toc 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RandVar/inst/doc/RandVar.toc 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,3 +0,0 @@
-\contentsline {section}{\numberline {1}{\tt S4} Classes}{2}{section.1}
-\contentsline {section}{\numberline {2}Functions and Methods}{3}{section.2}
-\contentsline {section}{\numberline {3}Odds and Ends}{9}{section.3}
Modified: pkg/RobAStBase/DESCRIPTION
===================================================================
--- pkg/RobAStBase/DESCRIPTION 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/DESCRIPTION 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,13 +1,12 @@
Package: RobAStBase
Version: 0.1.0
-Date: 2008-02-26
+Date: 2008-07-21
Title: Robust Asymptotic Statistics
Description: Base S4-classes and functions for robust asymptotic statistics.
Depends: R(>= 2.6.0), methods, distr(>= 2.0), distrEx(>= 2.0), distrMod(>= 2.0), RandVar(>= 0.6.2)
Author: Matthias Kohl, Peter Ruckdeschel
Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
-SaveImage: no
LazyLoad: yes
License: GPL version 2 or later
-URL: http://www.stamats.de/RobASt.htm
+URL: http://robast.r-forge.r-project.org/
Packaged: Thu Jan 3 20:00:08 2008; btm722
Modified: pkg/RobAStBase/R/AllClass.R
===================================================================
--- pkg/RobAStBase/R/AllClass.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/AllClass.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -53,7 +53,7 @@
# Weights
setClass("RobAStControl", representation(name ="character"),
contains = "VIRTUAL")
-
+
setClass("RobWeight", representation(name = "character", weight = "function"),
prototype(name = "some weight", weight = function(x) 1))
setClass("BoundedWeight", representation(clip = "numeric"),
Modified: pkg/RobAStBase/R/AllGeneric.R
===================================================================
--- pkg/RobAStBase/R/AllGeneric.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/AllGeneric.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -115,7 +115,7 @@
}
if(!isGeneric("weight<-")){
setGeneric("weight<-",
- function(object, value, ...) standardGeneric("weight<-"))
+ function(object, value, ...) standardGeneric("weight<-"))
}
if(!isGeneric("clip")){
setGeneric("clip",
Modified: pkg/RobAStBase/R/ContIC.R
===================================================================
--- pkg/RobAStBase/R/ContIC.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/ContIC.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -59,7 +59,7 @@
normtype <- res$normtype
biastype <- res$biastype
if(is.null(res$w)) res$w <- new("HampelWeight")
- w <- res$w
+ w <- res$w
return(ContIC(
name = "IC of contamination type",
CallL2Fam = call("L2ParamFamily",
@@ -100,8 +100,14 @@
function(object, value){
stopifnot(is.numeric(value))
L2Fam <- eval(object at CallL2Fam)
+ w <- object at weight
+ clip(w) <- value
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = object at neighborRadius),
+ biastype = object at biastype,
+ normW = object at normtype)
res <- list(A = object at stand, a = object at cent, b = value, d = object at lowerCase,
- risk = object at Risks, info = object at Infos)
+ risk = object at Risks, info = object at Infos, w = w,
+ normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = ContNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("clip<-", "The clipping bound has been changed")
@@ -112,8 +118,14 @@
function(object, value){
stopifnot(is.numeric(value))
L2Fam <- eval(object at CallL2Fam)
+ w <- object at weight
+ cent(w) <- as.vector(solve(object at stand) %*% value)
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = object at neighborRadius),
+ biastype = object at biastype,
+ normW = object at normtype)
res <- list(A = object at stand, a = value, b = object at clip, d = object at lowerCase,
- risk = object at Risks, info = object at Infos)
+ risk = object at Risks, info = object at Infos, w = w,
+ normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = ContNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("cent<-", "The centering constant has been changed")
@@ -124,8 +136,14 @@
function(object, value){
stopifnot(is.matrix(value))
L2Fam <- eval(object at CallL2Fam)
+ w <- object at weight
+ stand(w) <- value
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = object at neighborRadius),
+ biastype = object at biastype,
+ normW = object at normtype)
res <- list(A = value, a = object at cent, b = object at clip, d = object at lowerCase,
- risk = object at Risks, info = object at Infos)
+ risk = object at Risks, info = object at Infos, w = w,
+ normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = ContNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("stand<-", "The standardizing matrix has been changed")
@@ -137,7 +155,8 @@
stopifnot(is.null(value)||is.numeric(value))
L2Fam <- eval(object at CallL2Fam)
res <- list(A = object at stand, a = object at cent, b = object at clip, d = value,
- risk = object at Risks, info = object at Infos)
+ risk = object at Risks, info = object at Infos, w = object at weight,
+ normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = ContNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("lowerCase<-", "The slot 'lowerCase' has been changed")
@@ -148,7 +167,8 @@
function(object, value){
L2Fam <- eval(value)
res <- list(A = object at stand, a = object at cent, b = object at clip, d = object at lowerCase,
- risk = object at Risks, info = object at Infos)
+ risk = object at Risks, info = object at Infos, w = object at weight,
+ normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = ContNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("CallL2Fam<-", "The slot 'CallL2Fam' has been changed")
Modified: pkg/RobAStBase/R/TotalVarIC.R
===================================================================
--- pkg/RobAStBase/R/TotalVarIC.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/TotalVarIC.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -90,8 +90,14 @@
function(object, value){
stopifnot(is.numeric(value))
L2Fam <- eval(object at CallL2Fam)
+ w <- object at weight
+ clip(w)[1] <- value
+ weight(w) <- getweight(w, neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
+ biastype = object at biastype,
+ normW = object at normtype)
res <- list(A = object at stand, a = value, b = object at clipUp-value,
- d = object at lowerCase, risk = object at Risks, info = object at Infos)
+ d = object at lowerCase, risk = object at Risks, info = object at Infos,
+ w = w, normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("clipLo<-", "The lower clipping bound has been changed")
@@ -102,8 +108,14 @@
function(object, value){
stopifnot(is.numeric(value))
L2Fam <- eval(object at CallL2Fam)
+ w <- object at weight
+ clip(w)[2] <- value
+ weight(w) <- getweight(w, neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
+ biastype = object at biastype,
+ normW = object at normtype)
res <- list(A = object at stand, a = object at clipLo, b = value-object at clipLo,
- d = object at lowerCase, risk = object at Risks, info = object at Infos)
+ d = object at lowerCase, risk = object at Risks, info = object at Infos,
+ w = w, normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("clipUp<-", "The upper clipping bound has been changed")
@@ -114,8 +126,14 @@
function(object, value){
stopifnot(is.matrix(value))
L2Fam <- eval(object at CallL2Fam)
+ w <- object at weight
+ stand(w) <- value
+ weight(w) <- getweight(w, neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
+ biastype = object at biastype,
+ normW = object at normtype)
res <- list(A = value, a = object at clipLo, b = object at clipUp-object@clipLo,
- d = object at lowerCase, risk = object at Risks, info = object at Infos)
+ d = object at lowerCase, risk = object at Risks, info = object at Infos,
+ w = w, normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("stand<-", "The standardizing matrix has been changed")
@@ -127,7 +145,8 @@
stopifnot(is.null(value)||is.numeric(value))
L2Fam <- eval(object at CallL2Fam)
res <- list(A = object at stand, a = object at clipLo, b = object at clipUp-object@clipLo,
- d = value, risk = object at Risks, info = object at Infos)
+ d = value, risk = object at Risks, info = object at Infos,
+ w = object at weight, normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("lowerCase<-", "The slot 'lowerCase' has been changed")
@@ -138,7 +157,8 @@
function(object, value){
L2Fam <- eval(value)
res <- list(A = object at stand, a = object at clipLo, b = object at clipUp-object@clipLo,
- d = object at lowerCase, risk = object at Risks, info = object at Infos)
+ d = object at lowerCase, risk = object at Risks, info = object at Infos,
+ w = object at weight, normtype = object at normtype, biastype = object at biastype)
object <- generateIC(neighbor = TotalVarNeighborhood(radius = object at neighborRadius),
L2Fam = L2Fam, res = res)
addInfo(object) <- c("CallL2Fam<-", "The slot 'CallL2Fam' has been changed")
Modified: pkg/RobAStBase/R/Weights.R
===================================================================
--- pkg/RobAStBase/R/Weights.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/Weights.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,29 +1,41 @@
-.eq <-function(x,y = 0*x, tol = 1e-7) abs(x-y)<tol
-
setMethod("name", "RobWeight", function(object) object at name)
-setReplaceMethod("name", "RobWeight", function(object,value)
- {object at name <- value; object})
+setReplaceMethod("name", "RobWeight",
+ function(object, value){
+ object at name <- value
+ object
+ })
setMethod("clip", "BoundedWeight", function(object) object at clip)
-setReplaceMethod("clip", "BoundedWeight", function(object,value)
- {object at clip <- value; object})
+setReplaceMethod("clip", "BoundedWeight",
+ function(object, value){
+ object at clip <- value
+ object
+ })
setMethod("stand", "BdStWeight", function(object) object at stand)
-setReplaceMethod("stand", "BdStWeight", function(object,value)
- {object at stand <- value; object})
+setReplaceMethod("stand", "BdStWeight",
+ function(object, value){
+ object at stand <- value
+ object
+ })
setMethod("cent", "HampelWeight", function(object) object at cent)
-setReplaceMethod("cent", "HampelWeight", function(object,value)
- {object at cent <- value; object})
+setReplaceMethod("cent", "HampelWeight",
+ function(object, value){
+ object at cent <- value
+ object
+ })
setMethod("weight", "RobWeight", function(object) object at weight)
+setReplaceMethod("weight", "RobWeight",
+ function(object, value){
+ object at weight <- value
+ object
+ })
-setReplaceMethod("weight", "RobWeight", function(object,value)
- {object at weight <- value; object})
-
setMethod("getweight",
signature(Weight = "HampelWeight", neighbor = "ContNeighborhood",
biastype = "BiasType"),# normtype = "NormType"),
@@ -104,7 +116,7 @@
function(x){
y <- A%*%(x-z)
norm0 <- fct(normW)(y)
- ind <- 1-.eq(norm0)
+ ind <- 1-.eq(norm0)
ind*b/(norm0+1-ind)
}
}
@@ -124,7 +136,7 @@
y <- A*(x-z)
indp <- (y>0)
ind0 <- .eq(y)
- indm <- (y<0)
+ indm <- (y<0)
indm*b1/(y+ind0) + indp*b2/(y+ind0)
}
}
@@ -159,7 +171,7 @@
y <- A*x
indp <- (y>0)
ind0 <- .eq(y)
- indm <- (y<0)
+ indm <- (y<0)
indm*b1/(y+ind0) + indp*b2/(y+ind0)
}
}
Modified: pkg/RobAStBase/R/getRiskIC.R
===================================================================
--- pkg/RobAStBase/R/getRiskIC.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/getRiskIC.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -52,8 +52,8 @@
if(dimension(Domain(IC at Curve[[1]])) != dimension(img(L2Fam at distribution)))
stop("dimension of 'Domain' of 'Curve' != dimension of 'img' of 'distribution' of 'L2Fam'")
- trCov <- getRiskIC(IC, risk = asCov(), L2Fam = L2Fam)$trCov
- trCov$value <- sum(diag(std %*% trCov$value))
+ trCov <- getRiskIC(IC, risk = asCov(), L2Fam = L2Fam)$asCov
+ trCov$value <- sum(diag(trCov$value))
prec <- checkIC(IC, L2Fam, out = FALSE)
if(prec > tol)
Modified: pkg/RobAStBase/R/infoPlot.R
===================================================================
--- pkg/RobAStBase/R/infoPlot.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/R/infoPlot.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -22,7 +22,7 @@
trafo <- L2Fam at param@trafo
dims <- nrow(trafo)
-
+
QFc <- diag(dims)
if(is(object,"ContIC") & dims>1 )
{if (is(normtype(object),"QFNorm")) QFc <- QuadForm(normtype(object))
@@ -67,7 +67,7 @@
w0 <- options("warn")
options(warn = -1)
opar <- par()
- get(getOption("device"))()
+ devNew()
par(mfrow = c(nrows, ncols))
IC1.i.5 <- QF.5%*%IC1
classIC.i.5 <- QFc.5%*%classIC
Modified: pkg/RobAStBase/man/BdStWeight-class.Rd
===================================================================
--- pkg/RobAStBase/man/BdStWeight-class.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/man/BdStWeight-class.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -29,8 +29,10 @@
accessor function for slot \code{stand}. }
\item{stand<-}{\code{signature(object = "BdStWeight", value = "matrix")}:
- replacement function for slot \code{stand}. }
-
+ replacement function for slot \code{stand}. This replacement method
+ should be used with great care, as the slot \code{weight} is not
+ simultaneously updated and hence, this may lead to inconsistent
+ objects.}
}
}
\references{
Modified: pkg/RobAStBase/man/BoundedWeight-class.Rd
===================================================================
--- pkg/RobAStBase/man/BoundedWeight-class.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/man/BoundedWeight-class.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -25,8 +25,10 @@
accessor function for slot \code{clip}. }
\item{clip<-}{\code{signature(object = "BoundedWeight", value = "numeric")}:
- replacement function for slot \code{clip}. }
-
+ replacement function for slot \code{clip}. This replacement method
+ should be used with great care, as the slot \code{weight} is not
+ simultaneously updated and hence, this may lead to inconsistent
+ objects. }
}
}
\references{
Modified: pkg/RobAStBase/man/ContIC-class.Rd
===================================================================
--- pkg/RobAStBase/man/ContIC-class.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/man/ContIC-class.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -59,8 +59,10 @@
\item{\code{weight}:}{ object of class \code{"HampelWeight"}:
weight function }
+
\item{\code{biastype}:}{ object of class \code{"BiasType"}:
bias type (symmetric/onsided/asymmetric) }
+
\item{\code{normtype}:}{ object of class \code{"NormType"}:
norm type (Euclidean, information/self-standardized)}
Modified: pkg/RobAStBase/man/HampelWeight-class.Rd
===================================================================
--- pkg/RobAStBase/man/HampelWeight-class.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/man/HampelWeight-class.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -31,8 +31,10 @@
accessor function for slot \code{cent}. }
\item{cent<-}{\code{signature(object = "HampelWeight", value = "matrix")}:
- replacement function for slot \code{cent}. }
-
+ replacement function for slot \code{cent}. This replacement method
+ should be used with great care, as the slot \code{weight} is not
+ simultaneously updated and hence, this may lead to inconsistent
+ objects. }
}
}
\references{
Modified: pkg/RobAStBase/man/RobWeight-class.Rd
===================================================================
--- pkg/RobAStBase/man/RobWeight-class.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobAStBase/man/RobWeight-class.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -3,11 +3,11 @@
\alias{RobWeight-class}
\alias{name,RobWeight-method}
\alias{name<-,RobWeight-method}
+\alias{weight}
\alias{weight,RobWeight-method}
-\alias{weight}
+\alias{weight<-}
\alias{weight<--methods}
\alias{weight<-,RobWeight-method}
-\alias{weight<-}
\title{Robust Weight classes}
\description{Classes for robust weights.}
@@ -25,13 +25,13 @@
\item{name}{\code{signature(object = "RobWeight")}:
accessor function for slot \code{name}. }
- \item{name<-}{\code{signature(object = "RobWeight", value = "character")}:
+ \item{name<-}{\code{signature(object = "RobWeight")}:
replacement function for slot \code{name}. }
\item{weight}{\code{signature(object = "RobWeight")}:
accessor function for slot \code{weight}. }
- \item{weight<-}{\code{signature(object = "RobWeight", value = "ANY")}:
+ \item{weight<-}{\code{signature(object = "RobWeight")}:
replacement function for slot \code{weight}. }
}
}
Modified: pkg/RobLox/DESCRIPTION
===================================================================
--- pkg/RobLox/DESCRIPTION 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/DESCRIPTION 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,15 +1,15 @@
Package: RobLox
Version: 0.6.0
-Date: 2008-02-26
+Date: 2008-07-21
Title: Optimally robust influence curves for location and scale
Description: functions for the determination of optimally
robust influence curves in case of normal
location with unknown scale
Depends: R(>= 2.6.0), stats, distrMod(>= 2.0.0), RobAStBase(>= 0.1.0)
+Suggests: Biobase
Author: Matthias Kohl <Matthias.Kohl at stamats.de>
Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
-SaveImage: no
LazyLoad: yes
License: GPL version 2 or later
-URL: http://www.stamats.de/RobASt.htm
+URL: http://robast.r-forge.r-project.org/
Packaged: Mon Aug 6 08:07:25 2007; kohl
Modified: pkg/RobLox/R/rlOptIC.R
===================================================================
--- pkg/RobLox/R/rlOptIC.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/R/rlOptIC.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -10,11 +10,20 @@
A <- sd^2*A1
if(computeIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- 0
+ stand(w) <- as.matrix(A)
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
+
return(generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = 0, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = A - r^2*b^2),
- info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"))))
+ info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType())))
}else{
return(list(A = A, a = 0, b = b))
}
Modified: pkg/RobLox/R/rlsOptIC_AL.R
===================================================================
--- pkg/RobLox/R/rlsOptIC_AL.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/R/rlsOptIC_AL.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -127,11 +127,20 @@
if(computeIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- c(0, a2-1)
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
+
return(generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationScaleFamily(mean = mean, sd = sd),
- res = list(A = A, a = a, b = b, d = NULL,
- risk = list(asMSE = mse, asBias = b, asCov = mse - r^2*b^2),
- info = c("rlsOptIC.AL", "optimally robust IC for AL estimators and 'asMSE'"))))
+ res = list(A = as.matrix(A), a = a, b = b, d = NULL,
+ risk = list(asMSE = mse, asBias = b, trAsCov = mse - r^2*b^2),
+ info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType())))
}else{
return(list(A = A, a = a, b = b))
}
Modified: pkg/RobLox/R/roblox.R
===================================================================
--- pkg/RobLox/R/roblox.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/R/roblox.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -242,11 +242,19 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A2
+ stand(w) <- diag(c(A1, A2))
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationScaleFamily(mean = mean, sd = sd),
res = list(A = diag(c(A1, A2)), a = a, b = b, d = NULL,
risk = list(asMSE = mse, asBias = b, asCov = mse - r^2*b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix, "optIC" = IC1),
class = c("ALEstimate", "Estimate")))
}else
@@ -296,11 +304,19 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A2
+ stand(w) <- diag(c(A1, A2))
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationScaleFamily(mean = mean, sd = sd),
res = list(A = diag(c(A1, A2)), a = a, b = b, d = NULL,
risk = list(asMSE = mse, asBias = b, asCov = mse - r^2*b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
Infos(IC1) <- matrix(c(rep("roblox", 3),
paste("radius-minimax IC for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
@@ -315,6 +331,8 @@
}
}else{
if(missing(mean)){
+ if(length(sd) != 1)
+ stop("'sd' has length != 1")
if(sd <= 0)
stop("'sd' has to be positive")
if(missing(initial.est)){
@@ -341,11 +359,19 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- 0
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = 0, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix, "optIC" = IC1),
class = c("ALEstimate", "Estimate")))
}else
@@ -387,11 +413,19 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- 0
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormLocationFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = 0, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
Infos(IC1) <- matrix(c(rep("roblox", 3),
paste("radius-minimax IC for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
@@ -406,6 +440,8 @@
}
}
if(missing(sd)){
+ if(length(mean) != 1)
+ stop("mean has length != 1")
if(missing(initial.est)){
sd <- mad(x, na.rm = TRUE)
if(sd == 0)
@@ -437,11 +473,19 @@
"and 'asMSE'")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormScaleFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = a, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix, "optIC" = IC1),
class = c("ALEstimate", "Estimate")))
}else
@@ -485,11 +529,19 @@
paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
if(returnIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- a/A
+ stand(w) <- A
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
IC1 <- generateIC(neighbor = ContNeighborhood(radius = r),
L2Fam = NormScaleFamily(mean = mean, sd = sd),
res = list(A = as.matrix(A), a = a, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = b^2),
- info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'")))
+ info = c("roblox", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType()))
Infos(IC1) <- matrix(c(rep("roblox", 3),
paste("radius-minimax IC for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
Modified: pkg/RobLox/R/rowRoblox.R
===================================================================
--- pkg/RobLox/R/rowRoblox.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/R/rowRoblox.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -111,8 +111,13 @@
if(missing(mean) && missing(sd)){
if(missing(initial.est)){
- mean <- apply(x, 1, median, na.rm = TRUE)
- sd <- apply(x, 1, mad, na.rm = TRUE)
+ if(require(Biobase)){
+ mean <- rowMedians(x, na.rm = TRUE)
+ sd <- rowMedians(abs(x-mean), na.rm = TRUE)/qnorm(0.75)
+ }else{
+ mean <- apply(x, 1, median, na.rm = TRUE)
+ sd <- apply(abs(x-mean), 1, median, na.rm = TRUE)/qnorm(0.75)
+ }
if(any(sd == 0))
stop("'mad(x, na.rm = TRUE) == 0' => cannot compute a valid initial estimate,
please specify one via 'initial.est'")
@@ -192,7 +197,7 @@
paste("radius-minimax estimates for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
paste("least favorable contamination: ", round(r/sqrtn, 3), sep = ""),
- paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
+ paste("maximum MSE-inefficiency: ", round(ineff[1], 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix),
class = c("ALEstimate", "Estimate")))
@@ -201,11 +206,15 @@
if(missing(mean)){
if(any(sd <= 0))
stop("'sd' has to be positive")
+ if(!is.numeric(sd) || (length(sd) != 1 && length(sd) != nrow(x)))
+ stop("'sd' has wrong dimension")
if(missing(initial.est)){
- mean <- apply(x, 1, median, na.rm = TRUE)
+ if(require(Biobase)){
+ mean <- rowMedians(x, na.rm = TRUE)
+ }else{
+ mean <- apply(x, 1, median, na.rm = TRUE)
+ }
}else{
- if(!is.numeric(sd) || (length(sd) != 1 && length(sd) != nrow(x)))
- stop("'sd' has wrong dimension")
if(!is.numeric(initial.est) || length(initial.est) != nrow(x))
stop("'initial.est' has wrong dimension")
mean <- initial.est
@@ -220,8 +229,8 @@
A <- sd^2*.getA.loc(r)
b <- sd*.getb.loc(r)
}
- robEst <- .kstep.loc.matrix(x = x, initial.est = mean, A = A, b = b, sd = sd, k = k)
- names(robEst) <- c("mean")
+ robEst <- as.matrix(.kstep.loc.matrix(x = x, initial.est = mean, A = A, b = b, sd = sd, k = k))
+ colnames(robEst) <- "mean"
Info.matrix <- matrix(c("roblox",
paste("optimally robust estimates for contamination 'eps' =", round(eps, 3),
"and 'asMSE'")),
@@ -255,27 +264,32 @@
ineff <- (A - b^2*(r^2 - rlo^2))/Alo
}
}
- robEst <- .kstep.loc.matrix(x = x, initial.est = mean, A = A, b = b, sd = sd, k = k)
- names(robEst) <- c("mean")
+ robEst <- as.matrix(.kstep.loc.matrix(x = x, initial.est = mean, A = A, b = b, sd = sd, k = k))
+ colnames(robEst) <- "mean"
Info.matrix <- matrix(c(rep("roblox", 3),
paste("radius-minimax estimates for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
paste("least favorable contamination: ", round(r/sqrtn, 3), sep = ""),
- paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
+ paste("maximum MSE-inefficiency: ", round(ineff[1], 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix),
class = c("ALEstimate", "Estimate")))
}
}
if(missing(sd)){
- if(missing(initial.est)){
- sd <- apply(x, 1, mad, na.rm = TRUE)
+ if(!is.numeric(mean) || (length(mean) != 1 && length(mean) != nrow(x)))
+ stop("'mean' has wrong dimension")
+ if(missing(initial.est)){
+ if(require(Biobase)){
+ M <- rowMedians(x, na.rm = TRUE)
+ sd <- rowMedians(abs(x-M), na.rm = TRUE)/qnorm(0.75)
+ }else{
+ sd <- apply(x, 1, mad, na.rm = TRUE)
+ }
if(any(sd == 0))
stop("'mad(x, na.rm = TRUE) == 0' => cannot compute a valid initial estimate,
please specify one via 'initial.est'")
}else{
- if(!is.numeric(mean) || (length(mean) != 1 && length(mean) != nrow(x)))
- stop("'mean' has wrong dimension")
if(!is.numeric(initial.est) || length(initial.est) != nrow(x))
stop("'initial.est' has wrong dimension")
sd <- initial.est
@@ -294,8 +308,8 @@
a <- sd*.geta.sc(r)
b <- sd*.getb.sc(r)
}
- robEst <- .kstep.sc.matrix(x = x, initial.est = sd, A = A, a = a, b = b, mean = mean, k = k)
- names(robEst) <- c("sd")
+ robEst <- as.matrix(.kstep.sc.matrix(x = x, initial.est = sd, A = A, a = a, b = b, mean = mean, k = k))
+ colnames(robEst) <- "sd"
Info.matrix <- matrix(c("roblox",
paste("optimally robust estimates for contamination 'eps' =", round(eps, 3),
"and 'asMSE'")),
@@ -331,13 +345,13 @@
ineff <- (A - b^2*(r^2 - rlo^2))/Alo
}
}
- robEst <- .kstep.sc.matrix(x = x, initial.est = sd, A = A, a = a, b = b, mean = mean, k = k)
- names(robEst) <- c("sd")
+ robEst <- as.matrix(.kstep.sc.matrix(x = x, initial.est = sd, A = A, a = a, b = b, mean = mean, k = k))
+ colnames(robEst) <- "sd"
Info.matrix <- matrix(c(rep("roblox", 3),
paste("radius-minimax estimates for contamination interval [",
round(eps.lower, 3), ", ", round(eps.upper, 3), "]", sep = ""),
paste("least favorable contamination: ", round(r/sqrtn, 3), sep = ""),
- paste("maximum MSE-inefficiency: ", round(ineff, 3), sep = "")),
+ paste("maximum MSE-inefficiency: ", round(ineff[1], 3), sep = "")),
ncol = 2, dimnames = list(NULL, c("method", "message")))
return(structure(list("estimate" = robEst, "steps" = k, "Infos" = Info.matrix),
class = c("ALEstimate", "Estimate")))
Modified: pkg/RobLox/R/rsOptIC.R
===================================================================
--- pkg/RobLox/R/rsOptIC.R 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/R/rsOptIC.R 2008-07-24 17:07:22 UTC (rev 125)
@@ -62,11 +62,20 @@
A <- sd^2*A1
if(computeIC){
+ w <- new("HampelWeight")
+ clip(w) <- b
+ cent(w) <- z-1
+ stand(w) <- as.matrix(A)
+ weight(w) <- getweight(w, neighbor = ContNeighborhood(radius = r),
+ biastype = symmetricBias(),
+ normW = NormType())
+
return(generateIC(neighbor = ContNeighborhood(radius = r),
- L2Fam = NormScaleFamily(mean = mean, sd = sd),
+ L2Fam = NormScaleFamily(sd = sd, mean = mean),
res = list(A = as.matrix(A), a = a, b = b, d = NULL,
risk = list(asMSE = A, asBias = b, asCov = A - r^2*b^2),
- info = c("rsOptIC", "optimally robust IC for AL estimators and 'asMSE'"))))
+ info = c("rlOptIC", "optimally robust IC for AL estimators and 'asMSE'"),
+ w = w, biastype = symmetricBias(), normtype = NormType())))
}else{
return(list(A = A, a = a, b = b))
}
Modified: pkg/RobLox/man/roblox.Rd
===================================================================
--- pkg/RobLox/man/roblox.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/man/roblox.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -90,8 +90,7 @@
## amount of gross errors known
res1 <- roblox(x, eps = 0.05, returnIC = TRUE)
-res1$mean
-res1$sd
+res1$estimate
res1$optIC
checkIC(res1$optIC)
Risks(res1$optIC)
@@ -101,8 +100,7 @@
## amount of gross errors unknown
res2 <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE)
-res2$mean
-res2$sd
+res2$estimate
res2$optIC
checkIC(res2$optIC)
Risks(res2$optIC)
@@ -118,10 +116,10 @@
c(median(x), mad(x))
# optimally robust (amount of gross errors known)
-c(res1$mean, res1$sd)
+res1$estimate
# optimally robust (amount of gross errors unknown)
-c(res2$mean, res2$sd)
+res2$estimate
# Kolmogorov(-Smirnov) minimum distance estimator (robust)
(ks.est <- MDEstimator(x, ParamFamily = NormLocationScaleFamily(), distance = KolmogorovDist))
Modified: pkg/RobLox/man/rowRoblox.Rd
===================================================================
--- pkg/RobLox/man/rowRoblox.Rd 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobLox/man/rowRoblox.Rd 2008-07-24 17:07:22 UTC (rev 125)
@@ -33,8 +33,10 @@
scale with location specified, or both if neither is specified. The computation
uses a k-step construction with an appropriate initial estimate for location
or scale or location and scale, respectively. Valid candidates are e.g.
- median and/or MAD (default) as well as Kolmogorov(-Smirnov) or von Mises minimum
- distance estimators; cf. Rieder (1994) and Kohl (2005).
+ median and/or MAD (default) as well as Kolmogorov(-Smirnov) or Cram\'er von
+ Mises minimum distance estimators; cf. Rieder (1994) and Kohl (2005). In case
+ package Biobase from Bioconductor is installed as is suggested,
+ median and/or MAD are computed using function \code{rowMedians}.
These functions are optimized for the situation where one has a matrix
and wants to compute the optimally robust estimator for every row,
@@ -44,7 +46,7 @@
If the amount of gross errors (contamination) is known, it can be
specified by \code{eps}. The radius of the corresponding infinitesimal
contamination neighborhood is obtained by multiplying \code{eps}
- by the square root of the sample size.
+ by the square root of the sample size.
If the amount of gross errors (contamination) is unknown, try to find a
rough estimate for the amount of gross errors, such that it lies
@@ -64,7 +66,8 @@
In case of location, respectively scale one additionally has to specify
\code{sd}, respectively \code{mean} where \code{sd} and \code{mean} can
be a single number, i.e., identical for all rows, respectively columns,
- or a vector, i.e., different for all rows, respectively columns.
+ or a vector with length identical to the number of rows, respectively
+ columns.
}
\value{
An object of S3-class \code{"ALEstimate"} which inherits from
Modified: pkg/RobRex/DESCRIPTION
===================================================================
--- pkg/RobRex/DESCRIPTION 2008-07-24 16:57:59 UTC (rev 124)
+++ pkg/RobRex/DESCRIPTION 2008-07-24 17:07:22 UTC (rev 125)
@@ -1,6 +1,6 @@
Package: RobRex
-Version: 0.5.0
-Date: 2007-07-30
+Version: 0.6.0
+Date: 2008-07-21
Title: Optimally robust influence curves for regression and scale
Description: functions for the determination of optimally
robust influence curves in case of linear regression
@@ -9,8 +9,7 @@
Depends: R (>= 2.4.0), ROptRegTS(>= 0.5.0)
Author: Matthias Kohl <Matthias.Kohl at stamats.de>
Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
-SaveImage: no
LazyLoad: yes
License: GPL version 2 or later
URL: http://www.stamats.de/RobASt.htm
-Packaged: Mon Aug 6 08:18:03 2007; kohl
+Packaged: Mon Aug 6 08:18:03 2007; kohl
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