[Robast-commits] r43 - pkg/ROptEst/inst/scripts
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Feb 19 07:05:40 CET 2008
Author: stamats
Date: 2008-02-19 07:05:40 +0100 (Tue, 19 Feb 2008)
New Revision: 43
Added:
pkg/ROptEst/inst/scripts/tests.R
Removed:
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
Log:
problems with svn ...
Deleted: pkg/ROptEst/inst/scripts/BinomialModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/BinomialModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/BinomialModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,132 +0,0 @@
-###############################################################################
-## Example: Binomial Family
-###############################################################################
-require(ROptEst)
-
-## generates Binomial Family with
-## m = 25 and probability of success theta = 0.25
-B <- BinomFamily(size = 25, prob = 0.25)
-B # show B
-plot(B) # plot of Binom(size = 25, prob = 0.25) and L_2 derivative
-checkL2deriv(B)
-
-## classical optimal IC
-IC0 <- optIC(model = B, risk = asCov())
-IC0 # show IC
-plot(IC0) # plot IC
-checkIC(IC0)
-Risks(IC0)
-
-## lower case radius
-lowerCaseRadius(L2Fam = B, neighbor = ContNeighborhood(), risk = asMSE())
-lowerCaseRadius(L2Fam = B, neighbor = TotalVarNeighborhood(), risk = asMSE())
-
-## L_2 family + infinitesimal neighborhood
-RobB1 <- InfRobModel(center = B, neighbor = ContNeighborhood(radius = 0.5))
-RobB1 # show RobB1
-(RobB2 <- InfRobModel(center = B, neighbor = TotalVarNeighborhood(radius = 0.5)))
-
-## MSE solution
-system.time(IC1 <- optIC(model=RobB1, risk=asMSE()), gcFirst = TRUE)
-IC1
-checkIC(IC1)
-Risks(IC1)
-getRiskIC(IC1, asBias(), ContNeighborhood()) # standardized bias
-getRiskIC(IC1, asMSE(), ContNeighborhood(radius = 0.5))
-
-(Cov1 <- getRiskIC(IC1, asCov()))
-(mse1 <- getRiskIC(IC1, asMSE(), TotalVarNeighborhood(radius = 0.5)))
-(bias1 <- getRiskIC(IC1, asBias(), TotalVarNeighborhood()))
-## only suboptimal -> ToDo-List
-addRisk(IC1) <- list(Cov1, mse1, bias1)
-Risks(IC1)
-plot(IC1)
-
-system.time(IC2 <- optIC(model=RobB2, risk=asMSE()), gcFirst = TRUE)
-IC2
-checkIC(IC2)
-Risks(IC2)
-getRiskIC(IC2, asMSE(), TotalVarNeighborhood(radius = 0.5))
-getRiskIC(IC2, asBias(), TotalVarNeighborhood())
-getRiskIC(IC2, asBias(), ContNeighborhood())
-Cov2 <- getRiskIC(IC2, asCov())
-addRisk(IC2) <- Cov2
-Risks(IC2)
-plot(IC2)
-
-## lower case solutions
-(IC3 <- optIC(model=RobB1, risk=asBias()))
-checkIC(IC3)
-Risks(IC3)
-plot(IC3)
-
-(IC4 <- optIC(model=RobB2, risk=asBias()))
-checkIC(IC4)
-Risks(IC4)
-plot(IC4)
-
-
-## Hampel solution
-(IC5 <- optIC(model=RobB1, risk=asHampel(bound=clip(IC1))))
-checkIC(IC5)
-Risks(IC5)
-plot(IC5)
-
-(IC6 <- optIC(model=RobB2, risk=asHampel(bound=Risks(IC2)$asBias), maxiter = 200))
-checkIC(IC6)
-Risks(IC6)
-plot(IC6)
-
-
-## radius minimax IC
-system.time(IC7 <- radiusMinimaxIC(L2Fam=B, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=1), gcFirst = TRUE)
-IC7
-checkIC(IC7)
-Risks(IC7)
-plot(IC7)
-
-system.time(IC8 <- radiusMinimaxIC(L2Fam=B, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=1), gcFirst = TRUE)
-IC8
-checkIC(IC8)
-Risks(IC8)
-plot(IC8)
-
-
-## least favorable radius
-## (may take quite some time!)
-system.time(r.rho1 <- leastFavorableRadius(L2Fam=B, neighbor=ContNeighborhood(),
- risk=asMSE(), rho=0.5), gcFirst = TRUE)
-r.rho1
-system.time(r.rho2 <- leastFavorableRadius(L2Fam=B, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), rho=0.5), gcFirst = TRUE)
-r.rho2
-
-## one-step estimation
-## 1. generate a contaminated sample
-ind <- rbinom(100, size=1, prob=0.05)
-x <- rbinom(100, size=25, prob=(1-ind)*0.25 + ind*0.75)
-
-## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=x, Binom(size=25), param = "prob"))
-
-## 3. one-step estimation: radius known
-RobB3 <- InfRobModel(center=BinomFamily(size=25, prob=est0$prob),
- neighbor=ContNeighborhood(radius=0.5))
-IC9 <- optIC(model=RobB3, risk=asMSE())
-(est1 <- oneStepEstimator(x, IC=IC9, start=est0$prob))
-
-RobB4 <- InfRobModel(center=BinomFamily(size=25, prob=est0$prob),
- neighbor=TotalVarNeighborhood(radius=0.25))
-IC10 <- optIC(model=RobB4, risk=asMSE())
-(est1 <- oneStepEstimator(x, IC=IC10, start=est0$prob))
-
-## 4. one-step estimation: radius interval
-IC11 <- radiusMinimaxIC(L2Fam=BinomFamily(size=25, prob=est0$prob),
- neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(est2 <- oneStepEstimator(x, IC=IC11, start=est0$prob))
-
-IC12 <- radiusMinimaxIC(L2Fam=BinomFamily(size=25, prob=est0$prob),
- neighbor=TotalVarNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(est2 <- oneStepEstimator(x, IC=IC12, start=est0$prob))
Deleted: pkg/ROptEst/inst/scripts/ExponentialScaleModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/ExponentialScaleModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/ExponentialScaleModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,88 +0,0 @@
-###############################################################################
-## Example: Exponential Scale Family
-###############################################################################
-require(ROptEst)
-
-## generates Exponential Scale Family with rate = 1
-E1 <- ExpScaleFamily(rate = 1)
-E1 # show E1
-plot(E1) # plot of Exp(rate = 1) and L_2 derivative
-checkL2deriv(E1)
-
-# classical optimal IC
-E1.IC0 <- optIC(model = E1, risk = asCov())
-E1.IC0 # show IC
-checkIC(E1.IC0)
-Risks(E1.IC0)
-plot(E1.IC0) # plot IC
-
-# L_2 family + infinitesimal neighborhood
-E1.Rob1 <- InfRobModel(center = E1, neighbor = ContNeighborhood(radius = 0.5))
-E1.Rob1 # show E1.Rob1
-E1.Rob2 <- InfRobModel(center = E1, neighbor = TotalVarNeighborhood(radius = 0.5))
-
-# MSE solution
-(E1.IC1 <- optIC(model=E1.Rob1, risk=asMSE()))
-checkIC(E1.IC1)
-Risks(E1.IC1)
-plot(E1.IC1)
-(E1.IC2 <- optIC(model=E1.Rob2, risk=asMSE()))
-checkIC(E1.IC2)
-Risks(E1.IC2)
-plot(E1.IC2)
-
-# lower case solutions
-(E1.IC3 <- optIC(model=E1.Rob1, risk=asBias()))
-checkIC(E1.IC3)
-Risks(E1.IC3)
-plot(E1.IC3)
-(E1.IC4 <- optIC(model=E1.Rob2, risk=asBias()))
-checkIC(E1.IC4)
-Risks(E1.IC4)
-plot(E1.IC4)
-
-# Hampel solution
-(E1.IC5 <- optIC(model=E1.Rob1, risk=asHampel(bound=clip(E1.IC1))))
-checkIC(E1.IC5)
-Risks(E1.IC5)
-plot(E1.IC5)
-(E1.IC6 <- optIC(model=E1.Rob2, risk=asHampel(bound=Risks(E1.IC2)$asBias), maxiter = 200))
-checkIC(E1.IC6)
-Risks(E1.IC6)
-plot(E1.IC6)
-
-# radius minimax IC
-(E1.IC7 <- radiusMinimaxIC(L2Fam=E1, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=0.5))
-checkIC(E1.IC7)
-Risks(E1.IC7)
-(E1.IC8 <- radiusMinimaxIC(L2Fam=E1, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=0.5))
-checkIC(E1.IC8)
-Risks(E1.IC8)
-
-# 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(),
- risk=asMSE(), rho=1/3))
-
-## one-step estimation
-## 1. generate a contaminated sample
-ind <- rbinom(1e2, size=1, prob=0.05)
-E1.x <- rexp(1e2, rate=(1-ind)*2+ind*10)
-
-## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(E1.est0 <- ksEstimator(x=E1.x, Exp()))
-
-## 3. one-step estimation: radius known
-E1.Rob3 <- InfRobModel(center=ExpScaleFamily(rate=E1.est0$rate),
- neighbor=ContNeighborhood(radius=0.5))
-E1.IC9 <- optIC(model=E1.Rob3, risk=asMSE())
-(E1.est1 <- oneStepEstimator(E1.x, IC=E1.IC9, start=E1.est0$rate))
-
-## 4. one-step estimation: radius interval
-E1.IC10 <- radiusMinimaxIC(L2Fam=ExpScaleFamily(rate=E1.est0$rate),
- neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(E1.est2 <- oneStepEstimator(E1.x, IC=E1.IC10, start=E1.est0$rate))
Deleted: pkg/ROptEst/inst/scripts/GammaModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/GammaModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/GammaModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,73 +0,0 @@
-###############################################################################
-## Example: Gamma Family
-###############################################################################
-require(ROptEst)
-
-## generates Gamma Family with
-## scale = 1 and shape = 1
-G <- GammaFamily(scale = 1, shape = 2)
-G # show G
-plot(G) # plot of Gammad(scale = 1, shape = 2) and L_2 derivative
-checkL2deriv(G)
-
-## classical optimal IC
-IC0 <- optIC(model = G, risk = asCov())
-IC0 # show IC
-system.time(checkIC(IC0), gcFirst = TRUE)
-Risks(IC0)
-plot(IC0) # plot IC
-
-## L_2 family + infinitesimal neighborhood
-RobG1 <- InfRobModel(center = G, neighbor = ContNeighborhood(radius = 0.5))
-RobG1 # show RobB1
-
-## MSE solution
-system.time(IC1 <- optIC(model=RobG1, risk=asMSE()), gcFirst = TRUE)
-IC1
-checkIC(IC1)
-Risks(IC1)
-plot(IC1)
-x11()
-infoPlot(IC1)
-
-## lower case solutions
-system.time(IC2 <- optIC(model=RobG1, risk=asBias(), tol = 1e-10), gcFirst = TRUE)
-IC2
-checkIC(IC2)
-Risks(IC2)
-plot(IC2)
-x11()
-infoPlot(IC2)
-
-## Hampel solution
-system.time(IC3 <- optIC(model=RobG1, risk=asHampel(bound=clip(IC1))), gcFirst = TRUE)
-IC3
-checkIC(IC3)
-Risks(IC3)
-plot(IC3)
-x11()
-infoPlot(IC3)
-
-## 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)
-
-## least favorable radius
-## takes quite some 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
-
-## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=x, Gammad()))
-
-## 3. one-step estimation: radius known
-RobG3 <- InfRobModel(center=GammaFamily(scale = est0$scale, shape = est0$shape),
- neighbor=ContNeighborhood(radius=0.5))
-IC9 <- optIC(model=RobG3, risk=asMSE())
-(est1 <- oneStepEstimator(x, IC=IC9, start=est0))
Deleted: pkg/ROptEst/inst/scripts/GumbelLocationModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/GumbelLocationModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/GumbelLocationModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,135 +0,0 @@
-###############################################################################
-## Example: Gumbel Location Family
-## computations numerically less stable than in case of the
-## Exponential Scale Family
-###############################################################################
-require(ROptEst)
-
-## 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)
-G0 # show G0
-plot(G0) # plot of Gumbel(loc = 0, scale = 1) and L_2 derivative
-checkL2deriv(G0)
-
-# 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
-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
-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())
-checkIC(G0.IC1)
-Risks(G0.IC1)
-clip(E1.IC1)
-cent(E1.IC1)
-stand(E1.IC1)
-clip(G0.IC1)
-cent(G0.IC1)
-stand(G0.IC1)
-# alternatively
-G0.IC11 <- E1.IC1 # rate = 1!
-CallL2Fam(G0.IC11) <- call("GumbelLocationFamily")
-cent(G0.IC11) <- -cent(E1.IC1)
-G0.IC11
-checkIC(G0.IC11)
-Risks(G0.IC11)
-
-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)
-clipLo(E1.IC2)
-clipUp(E1.IC2)
-stand(E1.IC2)
-clipLo(G0.IC2)
-clipUp(G0.IC2)
-stand(G0.IC2)
-# alternatively
-G0.IC21 <- E1.IC2 # rate = 1!
-CallL2Fam(G0.IC21) <- call("GumbelLocationFamily")
-clipLo(G0.IC21) <- -clipUp(E1.IC2)
-clipUp(G0.IC21) <- -clipLo(E1.IC2)
-G0.IC21
-checkIC(G0.IC21)
-Risks(G0.IC21)
-
-# lower case solutions
-(G0.IC3 <- optIC(model=G0.Rob1, risk=asBias()))
-checkIC(G0.IC3)
-Risks(G0.IC3)
-(G0.IC4 <- optIC(model=G0.Rob2, risk=asBias()))
-checkIC(G0.IC4)
-Risks(G0.IC4)
-
-# Hampel solution
-(G0.IC5 <- optIC(model=G0.Rob1, risk=asHampel(bound=clip(G0.IC1))))
-checkIC(G0.IC5)
-Risks(G0.IC5)
-(G0.IC6 <- optIC(model=G0.Rob2, risk=asHampel(bound=Risks(G0.IC2)$asBias), maxiter = 100))
-checkIC(G0.IC6)
-Risks(G0.IC6)
-
-# radius minimax IC
-# numerically instable for small 'loRad'!
-# => use connection to ExpScaleFamily for computations
-#(G0.IC7 <- radiusMinimaxIC(L2Fam=G0, neighbor=ContNeighborhood(),
-# risk=asMSE(), loRad=0.5, upRad=1.0))
-#checkIC(G0.IC7)
-#Risks(G0.IC7)
-#(G0.IC8 <- radiusMinimaxIC(L2Fam=G0, neighbor=TotalVarNeighborhood(),
-# risk=asMSE(), loRad=0.5, upRad=1.0))
-#checkIC(G0.IC8)
-#Risks(G0.IC8)
-
-# least favorable radius
-# numerically instable!
-# => use connection to ExpScaleFamily for computations
-#(G0.r.rho1 <- leastFavorableRadius(L2Fam=G0, neighbor=ContNeighborhood(),
-# risk=asMSE(), rho=0.5))
-#(G0.r.rho2 <- leastFavorableRadius(L2Fam=G0, neighbor=TotalVarNeighborhood(),
-# risk=asMSE(), rho=1/3))
-
-## one-step estimation
-## 1. generate a contaminated sample
-ind <- rbinom(1e2, size=1, prob=0.05)
-G0.x <- rgumbel(1e2, loc=(1-ind)*0.5+ind*1)
-
-## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(G0.est0 <- ksEstimator(x=G0.x, Gumbel(), param = "loc"))
-
-## 3. one-step estimation: radius known
-G0.Rob3 <- InfRobModel(center=GumbelLocationFamily(loc=G0.est0$loc),
- neighbor=ContNeighborhood(radius=0.5))
-G0.IC9 <- optIC(model=G0.Rob3, risk=asMSE())
-(G0.est1 <- oneStepEstimator(G0.x, IC=G0.IC9, start=G0.est0$loc))
-
-## 4. M estimation: radius known
-G0.Rob31 <- InfRobModel(center=GumbelLocationFamily(loc=0),
- neighbor=ContNeighborhood(radius=0.5))
-G0.IC91 <- optIC(model=G0.Rob31, risk=asMSE())
-(G0.est11 <- locMEstimator(G0.x, IC=G0.IC91))
-
-## 5. one-step estimation: radius interval
-G0.IC10 <- radiusMinimaxIC(L2Fam=GumbelLocationFamily(loc=G0.est0$loc),
- neighbor=ContNeighborhood(), risk=asMSE(), loRad=0.5, upRad=1)
-(G0.est2 <- oneStepEstimator(G0.x, IC=G0.IC10, start=G0.est0$loc))
-
-## 6. M estimation: radius interval
-G0.IC101 <- radiusMinimaxIC(L2Fam=GumbelLocationFamily(),
- neighbor=ContNeighborhood(), risk=asMSE(), loRad=0.5, upRad=1)
-(G0.est21 <- locMEstimator(G0.x, IC=G0.IC101))
-
-distrExOptions(ElowerTruncQuantile, 0) # default
Deleted: pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/LognormalAndNormalModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,155 +0,0 @@
-###############################################################################
-## Example: Lognormal Scale and Normal Location
-###############################################################################
-require(ROptEst)
-
-## generates Lognormal Scale Family with rate = 1
-LN1 <- LnormScaleFamily()
-LN1 # show LN1
-plot(LN1) # plot of Exp(rate = 1) and L_2 derivative
-checkL2deriv(LN1)
-
-## generates Normal Location Family with mean = 0
-N0 <- NormLocationFamily(mean=0, sd=1)
-N0 # show G0
-plot(N0) # plot of Norm(mean = 0, sd = 1) and L_2 derivative
-checkL2deriv(N0)
-
-
-# classical optimal IC
-LN1.IC0 <- optIC(model = LN1, risk = asCov())
-LN1.IC0 # show IC
-plot(LN1.IC0) # plot IC
-checkIC(LN1.IC0)
-Risks(LN1.IC0)
-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
-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))
-N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 0.5))
-N0.Rob1 # show N0.Rob1
-N0.Rob2 <- InfRobModel(center = N0, neighbor = TotalVarNeighborhood(radius = 0.25))
-
-
-# MSE solution
-LN1.IC1 <- optIC(model=LN1.Rob1, risk=asMSE())
-checkIC(LN1.IC1)
-Risks(LN1.IC1)
-plot(LN1.IC1)
-
-N0.IC1 <- optIC(model=N0.Rob1, risk=asMSE())
-checkIC(N0.IC1)
-Risks(N0.IC1)
-plot(N0.IC1)
-
-clip(LN1.IC1)
-cent(LN1.IC1)
-stand(LN1.IC1)
-clip(N0.IC1)
-cent(N0.IC1)
-stand(N0.IC1)
-
-LN1.IC2 <- optIC(model=LN1.Rob2, risk=asMSE())
-checkIC(LN1.IC2)
-Risks(LN1.IC2)
-plot(LN1.IC2)
-
-N0.IC2 <- optIC(model=N0.Rob2, risk=asMSE())
-checkIC(N0.IC2)
-Risks(N0.IC2)
-plot(N0.IC2)
-
-clipLo(LN1.IC2)
-clipUp(LN1.IC2)
-stand(LN1.IC2)
-clipLo(N0.IC2)
-clipUp(N0.IC2)
-stand(N0.IC2)
-
-
-# lower case solutions
-LN1.IC3 <- optIC(model=LN1.Rob1, risk=asBias())
-checkIC(LN1.IC3)
-Risks(LN1.IC3)
-plot(LN1.IC3)
-
-N0.IC3 <- optIC(model=N0.Rob1, risk=asBias())
-checkIC(N0.IC3)
-Risks(N0.IC3)
-plot(N0.IC3)
-
-LN1.IC4 <- optIC(model=LN1.Rob2, risk=asBias())
-checkIC(LN1.IC4)
-Risks(LN1.IC4)
-plot(LN1.IC4)
-
-N0.IC4 <- optIC(model=N0.Rob2, risk=asBias())
-checkIC(N0.IC4)
-Risks(N0.IC4)
-plot(N0.IC4)
-
-
-# Hampel solution
-LN1.IC5 <- optIC(model=LN1.Rob1, risk=asHampel(bound=clip(LN1.IC1)))
-checkIC(LN1.IC5)
-Risks(LN1.IC5)
-plot(LN1.IC5)
-
-N0.IC5 <- optIC(model=N0.Rob1, risk=asHampel(bound=clip(N0.IC1)))
-checkIC(N0.IC5)
-Risks(N0.IC5)
-plot(N0.IC5)
-
-LN1.IC6 <- optIC(model=LN1.Rob2, risk=asHampel(bound=Risks(LN1.IC2)$asBias))
-checkIC(LN1.IC6)
-Risks(LN1.IC6)
-plot(LN1.IC6)
-
-N0.IC6 <- optIC(model=N0.Rob2, risk=asHampel(bound=Risks(N0.IC2)$asBias))
-checkIC(N0.IC6)
-Risks(N0.IC6)
-plot(N0.IC6)
-
-# radius minimax IC
-(LN1.IC7 <- radiusMinimaxIC(L2Fam=LN1, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=0.5))
-checkIC(LN1.IC7)
-Risks(LN1.IC7)
-plot(LN1.IC7)
-
-(N0.IC7 <- radiusMinimaxIC(L2Fam=N0, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0.1, upRad=0.5))
-checkIC(N0.IC7)
-Risks(N0.IC7)
-plot(N0.IC7)
-
-(LN1.IC8 <- radiusMinimaxIC(L2Fam=LN1, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=0.25))
-checkIC(LN1.IC8)
-Risks(LN1.IC8)
-plot(LN1.IC8)
-
-(N0.IC8 <- radiusMinimaxIC(L2Fam=N0, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=0.25))
-checkIC(N0.IC8)
-Risks(N0.IC8)
-plot(N0.IC8)
-
-
-# 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(),
- risk=asMSE(), rho=0.5))
-(LN1.r.rho2 <- leastFavorableRadius(L2Fam=LN1, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), rho=1/3))
-(N0.r.rho2 <- leastFavorableRadius(L2Fam=N0, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), rho=1/3))
Deleted: pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,79 +0,0 @@
-###############################################################################
-## Example: Normal location and scale
-###############################################################################
-require(ROptEst)
-
-## generates normal location and scale family with mean = 0 and sd = 1
-N0 <- NormLocationScaleFamily(mean=0, sd=1)
-N0 # show G0
-plot(N0) # plot of Norm(mean = 0, sd = 1) and L_2 derivative
-checkL2deriv(N0)
-
-# 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
-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)
-checkIC(N0.IC1)
-Risks(N0.IC1)
-plot(N0.IC1)
-infoPlot(N0.IC1)
-
-# lower case solutions
-(N0.IC2 <- optIC(model = N0.Rob1, risk = asBias(), tol = 1e-10))
-checkIC(N0.IC2)
-Risks(N0.IC2)
-plot(N0.IC2)
-infoPlot(N0.IC2)
-
-# 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!)
-(N0.IC4 <- radiusMinimaxIC(L2Fam=N0, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=Inf))
-checkIC(N0.IC4)
-Risks(N0.IC4)
-plot(N0.IC4)
-infoPlot(N0.IC4)
-
-# 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
-ind <- rbinom(100, size=1, prob=0.05)
-x <- rnorm(100, mean=0, sd=(1-ind) + ind*9)
-
-## 2. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=x, Norm()))
-
-## 3. one-step estimation: radius known
-N1 <- NormLocationScaleFamily(mean=est0$mean, sd=est0$sd)
-N1.Rob <- InfRobModel(center = N1, neighbor = ContNeighborhood(radius = 0.5))
-IC1 <- optIC(model = N1.Rob, risk = asMSE())
-(est1 <- oneStepEstimator(x, IC1, est0))
-
-## 4. one-step estimation: radius unknown
-## rough estimate: 1-10% contamination
-## => r\in[0.1,1.0]
-
-## takes some time
-IC2 <- radiusMinimaxIC(L2Fam=N1, neighbor=ContNeighborhood(),risk=asMSE(),
- loRad=0.1, upRad=1.0)
-(est2 <- oneStepEstimator(x, IC2, est0))
Deleted: pkg/ROptEst/inst/scripts/NormalScaleModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/NormalScaleModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/NormalScaleModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,72 +0,0 @@
-###############################################################################
-## Example: Normal Scale
-###############################################################################
-require(ROptEst)
-
-## generates Normal Scale Family with scale = 1
-N0 <- NormScaleFamily(mean=0, sd=1)
-N0 # show G0
-plot(N0) # plot of Norm(mean = 0, sd = 1) and L_2 derivative
-checkL2deriv(N0)
-
-# 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
-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
-(N0.IC1 <- optIC(model=N0.Rob1, risk=asMSE()))
-checkIC(N0.IC1)
-Risks(N0.IC1)
-plot(N0.IC1)
-
-(N0.IC2 <- optIC(model=N0.Rob2, risk=asMSE()))
-checkIC(N0.IC2)
-Risks(N0.IC2)
-plot(N0.IC2)
-
-# lower case solutions
-(N0.IC3 <- optIC(model=N0.Rob1, risk=asBias()))
-checkIC(N0.IC3)
-Risks(N0.IC3)
-plot(N0.IC3)
-(N0.IC4 <- optIC(model=N0.Rob2, risk=asBias()))
-checkIC(N0.IC4)
-Risks(N0.IC4)
-plot(N0.IC4)
-
-# Hampel solution
-(N0.IC5 <- optIC(model=N0.Rob1, risk=asHampel(bound=clip(N0.IC1))))
-checkIC(N0.IC5)
-Risks(N0.IC5)
-plot(N0.IC5)
-(N0.IC6 <- optIC(model=N0.Rob2, risk=asHampel(bound=Risks(N0.IC2)$asBias), maxiter = 200))
-checkIC(N0.IC6)
-Risks(N0.IC6)
-plot(N0.IC6)
-
-# radius minimax IC
-(N0.IC7 <- radiusMinimaxIC(L2Fam=N0, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=Inf))
-checkIC(N0.IC7)
-Risks(N0.IC7)
-plot(N0.IC7)
-(N0.IC8 <- radiusMinimaxIC(L2Fam=N0, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=Inf))
-checkIC(N0.IC8)
-Risks(N0.IC8)
-plot(N0.IC8)
-
-# 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(),
- risk=asMSE(), rho=1/3))
Deleted: pkg/ROptEst/inst/scripts/PoissonModel.R
===================================================================
--- pkg/ROptEst/inst/scripts/PoissonModel.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/PoissonModel.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,109 +0,0 @@
-###############################################################################
-## Example: Poisson Family
-###############################################################################
-require(ROptEst)
-
-distroptions("TruncQuantile", 1e-10) # increases numerical support of Pois;
- # i.e., increases precision of the
- # computations
-## generates Poisson Family with theta = 4.5
-P <- PoisFamily(lambda = 4.5)
-P # show P
-plot(P) # plot of Pois(lambda = 4.5) and L_2 derivative
-checkL2deriv(P)
-
-# 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
-RobP1 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 0.5))
-RobP1 # show RobP1
-(RobP2 <- InfRobModel(center = P, neighbor = TotalVarNeighborhood(radius = 0.5)))
-
-## lower case radius
-lowerCaseRadius(L2Fam = P, ContNeighborhood(radius = 0.5), risk = asMSE())
-lowerCaseRadius(L2Fam = P, TotalVarNeighborhood(radius = 0.5), risk = asMSE())
-
-# MSE solution
-(IC1 <- optIC(model=RobP1, risk=asMSE()))
-checkIC(IC1)
-Risks(IC1)
-plot(IC1)
-
-(IC2 <- optIC(model=RobP2, risk=asMSE()))
-checkIC(IC2)
-Risks(IC2)
-plot(IC2)
-
-
-# lower case solutions
-(IC3 <- optIC(model=RobP1, risk=asBias()))
-checkIC(IC3)
-Risks(IC3)
-plot(IC3)
-
-(IC4 <- optIC(model=RobP2, risk=asBias()))
-checkIC(IC4)
-Risks(IC4)
-plot(IC4)
-
-# Hampel solution
-(IC5 <- optIC(model=RobP1, risk=asHampel(bound=clip(IC1))))
-checkIC(IC5)
-Risks(IC5)
-plot(IC5)
-
-(IC6 <- optIC(model=RobP2, risk=asHampel(bound=Risks(IC2)$asBias), maxiter = 200))
-checkIC(IC6)
-Risks(IC6)
-plot(IC6)
-
-
-# radius minimax IC
-(IC7 <- radiusMinimaxIC(L2Fam=P, neighbor=ContNeighborhood(),
- risk=asMSE(), loRad=0, upRad=0.5))
-checkIC(IC7)
-Risks(IC7)
-plot(IC7)
-
-(IC8 <- radiusMinimaxIC(L2Fam=P, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), loRad=0, upRad=Inf))
-checkIC(IC8)
-Risks(IC8)
-plot(IC8)
-
-# least favorable radius
-# (may take quite some time!)
-(r.rho1 <- leastFavorableRadius(L2Fam=P, neighbor=ContNeighborhood(),
- risk=asMSE(), rho=0.5))
-(r.rho2 <- leastFavorableRadius(L2Fam=P, neighbor=TotalVarNeighborhood(),
- risk=asMSE(), rho=1/3))
-
-## one-step estimation
-## Example: Rutherford-Geiger (1910)
-## cf. Feller~(1968), Section VI.7 (a)
-x <- c(rep(0, 57), rep(1, 203), rep(2, 383), rep(3, 525), rep(4, 532),
- rep(5, 408), rep(6, 273), rep(7, 139), rep(8, 45), rep(9, 27),
- rep(10, 10), rep(11, 4), rep(12, 0), rep(13, 1), rep(14, 1))
-
-## 0. mean (classical optimal)
-(est0 <- mean(x))
-
-## 1. Kolmogorov(-Smirnov) minimum distance estimator
-(est1 <- ksEstimator(x=x, Pois()))
-
-## 2. one-step estimation: radius interval
-## 2.1 small amount of contamination < 2%
-IC9 <- radiusMinimaxIC(L2Fam=PoisFamily(lambda=est1$lambda),
- neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=1)
-(est21 <- oneStepEstimator(x, IC=IC9, start=est1$lambda))
-## 2.2 amount of contamination unknown
-IC10 <- radiusMinimaxIC(L2Fam=PoisFamily(lambda=est1$lambda),
- neighbor=ContNeighborhood(), risk=asMSE(), loRad=0, upRad=Inf)
-(est22 <- oneStepEstimator(x, IC=IC10, start=est1$lambda))
-
-distroptions("TruncQuantile", 1e-5) # default
Deleted: pkg/ROptEst/inst/scripts/UnderOverShootRisk.R
===================================================================
--- pkg/ROptEst/inst/scripts/UnderOverShootRisk.R 2008-02-19 05:59:47 UTC (rev 42)
+++ pkg/ROptEst/inst/scripts/UnderOverShootRisk.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -1,123 +0,0 @@
-###############################################################################
-## Example: Normal Location
-###############################################################################
-system.time(require(ROptEst))
-
-## generates Normal Location Family with mean = 0
-N0 <- NormLocationFamily(mean=0)
-n <- 100
-tau <- qnorm(0.975)
-
-## classical optimal IC (radius = 0!)
-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)
-checkIC(IC0c)
-Risks(IC0c)
-system.time(IC0v <- optIC(model=N0.Rob2, risk=asUnOvShoot(width = tau)), gcFirst = TRUE)
-checkIC(IC0v)
-Risks(IC0v)
-
-## boundary case
-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)
-checkIC(IC0c)
-Risks(IC0c)
-system.time(IC0v <- optIC(model=N0.Rob2, risk=asUnOvShoot(width = tau)), gcFirst = TRUE)
-checkIC(IC0v)
-Risks(IC0v)
-
-
-# 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
-N0.IC1 <- optIC(model = N0.Rob1, risk = asUnOvShoot(width = tau))
-checkIC(N0.IC1)
-Risks(N0.IC1)
-plot(N0.IC1)
-
-N0.IC2 <- optIC(model = N0.Rob2, risk = asUnOvShoot(width = tau))
-checkIC(N0.IC2)
-Risks(N0.IC2)
-plot(N0.IC2)
-
-# fiUnOvShoot solution
-N0.IC3 <- optIC(model=N0.Rob3, risk=fiUnOvShoot(width = tau/sqrt(n)), sampleSize = n)
-checkIC(N0.IC3)
-Risks(N0.IC3)
-plot(N0.IC3)
-
-N0.IC4 <- optIC(model=N0.Rob4, risk=fiUnOvShoot(width = tau/sqrt(n)), sampleSize = n)
-checkIC(N0.IC4)
-Risks(N0.IC4)
-plot(N0.IC4)
-
-# 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)))
-stand5 <- 1/(2*pnorm(clipUp5)-1)
-clipUp(N0.IC5) <- stand5*clipUp5
-clipLo(N0.IC5) <- -clipUp(N0.IC5)
-stand(N0.IC5) <- as.matrix(stand5)
-Infos(N0.IC5) <- matrix(c("manually", "O(n^(-1/2))-corrected solution"), ncol=2,
- dimnames=list(character(0), c("method", "message")))
-checkIC(N0.IC5)
-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
-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)))
-stand6 <- 1/(2*pnorm(clipUp6)-1)
-clipUp(N0.IC6) <- stand6*clipUp6
-clipLo(N0.IC6) <- -clipUp(N0.IC6)
-stand(N0.IC6) <- as.matrix(stand6)
-Infos(N0.IC6) <- matrix(c("manually", "O(n^(-1))-corrected solution"), ncol=2,
- dimnames=list(character(0), c("method", "message")))
-checkIC(N0.IC6)
-getRiskIC(N0.IC6, asUnOvShoot(width = tau), TotalVarNeighborhood(radius=rad/2))
-getRiskIC(N0.IC6, fiUnOvShoot(width = tau/sqrt(n)), TotalVarNeighborhood(radius=rad/2/sqrt(n)), sampleSize = n)
-
-
-## estimation
-## 1. generate a contaminated sample
-ind <- rbinom(1e2, size = 1, prob = 0.05)
-X <- rnorm(1e2, mean = ind*4)
-summary(X)
-
-## 2. M estimation
-N0.Rob5 <- InfRobModel(center = NormLocationFamily(mean = 0),
- 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)
-(Mest2 <- locMEstimator(X, IC=N0.IC8))
-
-
-## 3. Kolmogorov(-Smirnov) minimum distance estimator
-(est0 <- ksEstimator(x=X, Norm(), param = "mean"))
-
-## 4. one-step estimation
-N0.Rob7 <- InfRobModel(center = NormLocationFamily(mean = est0$mean),
- 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))
Added: pkg/ROptEst/inst/scripts/tests.R
===================================================================
--- pkg/ROptEst/inst/scripts/tests.R (rev 0)
+++ pkg/ROptEst/inst/scripts/tests.R 2008-02-19 06:05:40 UTC (rev 43)
@@ -0,0 +1,134 @@
+library(ROptEst)
+
+###############################################################################
+## start of tests
+###############################################################################
+
+## positive-definite, symmetric matrices
+new("PosDefSymmMatrix", diag(2))
+PosDefSymmMatrix(1)
+PosDefSymmMatrix(diag(3))
+
+
+## Distribution symmetry
+S1 <- new("NoSymmetry")
+type(S1)
+NoSymmetry()
+S2 <- new("EllipticalSymmetry", SymmCenter = 2)
+type(S2)
+EllipticalSymmetry(SymmCenter = 1)
+S3 <- new("SphericalSymmetry", SymmCenter = -2)
+type(S3)
+SphericalSymmetry(SymmCenter = c(0,0))
+new("DistrSymmList", list(S1, S2, S3))
+DistrSymmList(S1, S2, S3)
+
+## Distribution symmetry
+S4 <- new("NonSymmetric")
+type(S4)
+NonSymmetric()
+S5 <- new("EvenSymmetric", SymmCenter = -1)
+type(S5)
+EvenSymmetric(SymmCenter = 0)
+S6 <- new("OddSymmetric", SymmCenter = 3)
+type(S6)
+OddSymmetric(SymmCenter = c(1,1))
+new("FunSymmList", list(S4, S5, S6))
+FunSymmList(S4, S5, S6)
+
+
+## parametric family
+(PF <- new("ParamFamily"))
+plot(PF)
+ParamFamily()
+
+
+## L2-differentiable parametric family
+(L2PF <- new("L2ParamFamily"))
+plot(L2PF)
+L2ParamFamily()
+
+
+## simple L2-differentiable parametric families
+BinomFamily()
+BinomFamily(size = 10)
+BinomFamily(prob = 0.4)
+BinomFamily(size = 100, prob = 0.3)
+BinomFamily(size = 50, prob = 0.8, trafo = matrix(-1))
+
+PoisFamily()
+PoisFamily(lambda = 10)
+PoisFamily(lambda = 20, trafo = matrix(3))
+
+NormLocationFamily()
+NormLocationFamily(mean = 2)
+NormLocationFamily(sd = 0.1)
+NormLocationFamily(mean = -3, sd = 2)
+NormLocationFamily(mean = 1, sd = 0.5, trafo = matrix(-1))
+
+GumbelLocationFamily()
+GumbelLocationFamily(loc = -2)
+GumbelLocationFamily(scale = 2)
+GumbelLocationFamily(loc = 1, scale = 0.5)
+GumbelLocationFamily(loc = 10, scale = 10, trafo = matrix(0.5))
+
+NormScaleFamily()
+NormScaleFamily(sd = 3)
+NormScaleFamily(mean = 5)
+NormScaleFamily(sd = 0.1, mean = -3)
+NormScaleFamily(sd = 2.5, mean = 1, trafo = matrix(0.1))
+
+ExpScaleFamily()
+ExpScaleFamily(rate = 0.5)
+ExpScaleFamily(rate = 2, trafo = matrix(2))
+
+LnormScaleFamily()
+LnormScaleFamily(meanlog = 0.5)
+LnormScaleFamily(sdlog = 0.1)
+LnormScaleFamily(meanlog = -0.3, sdlog = 2)
+LnormScaleFamily(meanlog = 2, sdlog = 1.2, trafo = matrix(2.5))
+
+G1 <- GammaFamily()
+name(G1)
+name(G1) <- "standard Gamma family"
+name(G1)
+distribution(G1)
+(old <- props(G1))
+addProp(G1) <- "test"
+props(G1)
+props(G1) <- old
+props(G1)
+param(G1)
+main(G1)
+nuisance(G1)
+trafo(G1)
+L2deriv(G1)
+L2derivDistr(G1)
+L2derivSymm(G1)
+FisherInfo(G1)
+GammaFamily(scale = 2)
+GammaFamily(shape = 0.75)
+GammaFamily(scale = 1.5, shape = 2)
+GammaFamily(scale = 3, shape = 1.5, trafo = matrix(c(3, 0, 0, 1), ncol = 2))
+
+NormLocationScaleFamily()
+NormLocationScaleFamily(mean = 1)
+NormLocationScaleFamily(sd = 0.5)
+NormLocationScaleFamily(mean = -3, sd = 2)
+N1 <- NormLocationScaleFamily(mean = 2, sd = 0.1, trafo = matrix(c(1, 0), ncol = 2))
+plot(N1)
+
+## robust models
+new("FixRobModel")
+(RM1 <- FixRobModel(center = NormLocationFamily()))
+FixRobModel(center = PoisFamily(), neighbor = TotalVarNeighborhood(radius = 0.5))
+new("InfRobModel")
+(RM2 <- InfRobModel(center = NormLocationScaleFamily()))
+InfRobModel(center = BinomFamily(size=10), neighbor = TotalVarNeighborhood(radius = 0.2))
+
+
+###############################################################################
+## end of tests
+###############################################################################
+
+q("no")
Property changes on: pkg/ROptEst/inst/scripts/tests.R
___________________________________________________________________
Name: svn:executable
+ *
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