[Robast-commits] r1280 - branches/robast-1.3/pkg/ROptEst/inst/scripts
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Feb 6 21:21:54 CET 2024
Author: ruckdeschel
Date: 2024-02-06 21:21:54 +0100 (Tue, 06 Feb 2024)
New Revision: 1280
Added:
branches/robast-1.3/pkg/ROptEst/inst/scripts/examples_taking_longer.R
Modified:
branches/robast-1.3/pkg/ROptEst/inst/scripts/AnscombeOrNot.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/GammaModel.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/GumbelLocationModel.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R
branches/robast-1.3/pkg/ROptEst/inst/scripts/NormalLocationScaleModel.R
Log:
[ROptEst] ported changes in R-scripts in inst/scripts from trunk to devel branch
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/AnscombeOrNot.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/AnscombeOrNot.R 2024-02-06 20:20:56 UTC (rev 1279)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/AnscombeOrNot.R 2024-02-06 20:21:54 UTC (rev 1280)
@@ -42,7 +42,7 @@
contnb <- ContNeighborhood(radius = 0.5)
totvnb <- TotalVarNeighborhood(radius = 0.5)
medianmad <- list(function(x)sign(x),function(x)sign(abs(x)-qnorm(.75)))
-### Normal location and scale --- takes ~2min:
+### Normal location and scale --- takes ~20 sec:
system.time({print(round(mineff.ls <- checkOut(L2M = NormLocationScaleFamily(), nbd = contnb,
extraICs = list(medmad=medianmad)),3))})
### Normal location
@@ -51,9 +51,9 @@
system.time(print(round(mineff.sc <- checkOut(L2M = NormScaleFamily(), nbd = contnb),3)))
### Normal scale total variation
system.time(print(round(mineff.sv <- checkOut(L2M = NormScaleFamily(), nbd = totvnb),3)))
-### Poisson(lambda=1) convex contamination:
+### Poisson(lambda=1) convex contamination: ### ~ 13 sec
system.time(print(round(mineff.pc <- checkOut(L2M = PoisFamily(lambda = 1), nbd = contnb),3)))
-### Poisson(lambda=1) scale convex contamination:
+### Poisson(lambda=1) scale convex contamination: ~20 sec
system.time(print(round(mineff.pv <- checkOut(L2M = PoisFamily(lambda = 1), nbd = totvnb),3)))
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R 2024-02-06 20:20:56 UTC (rev 1279)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/BinomialModel.R 2024-02-06 20:21:54 UTC (rev 1280)
@@ -49,11 +49,11 @@
IC0 <- optIC(model = B, risk = asCov())
IC0 # show IC
-#An object of class ''IC''
+#An object of class “IC”
#### name: Classical optimal influence curve for Binomial family
#### L2-differentiable parametric family: Binomial family
#
-#### 'Curve': An object of class ''EuclRandVarList''
+#### 'Curve': An object of class “EuclRandVarList”
#Domain: Real Space with dimension 1
#[[1]]
#length of Map: 1
@@ -101,7 +101,7 @@
RobB1 <- InfRobModel(center = B, neighbor = ContNeighborhood(radius = 0.5))
RobB1 # show RobB1
-#An object of class ''InfRobModel''
+#An object of class “InfRobModel”
####### center: An object of class "BinomFamily"
#### name: Binomial family
#
@@ -122,13 +122,13 @@
#[1] "The Binomial family is symmetric with respect to prob = 0.5;"
#[2] "i.e., d(Binom(size, prob))(k)=d(Binom(size,1-prob))(size-k)"
#
-####### neighborhood: An object of class ''ContNeighborhood''
+####### neighborhood: An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
(RobB2 <- InfRobModel(center = B, neighbor = TotalVarNeighborhood(radius = 0.5)))
-#An object of class ''InfRobModel''
+#An object of class “InfRobModel”
####### center: An object of class "BinomFamily"
#### name: Binomial family
#
@@ -149,7 +149,7 @@
#[1] "The Binomial family is symmetric with respect to prob = 0.5;"
#[2] "i.e., d(Binom(size, prob))(k)=d(Binom(size,1-prob))(size-k)"
#
-####### neighborhood: An object of class ''TotalVarNeighborhood''
+####### neighborhood: An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -160,7 +160,10 @@
system.time(ICA <- optIC(model=RobB1, risk=asAnscombe(),
verbose=TRUE,lower=NULL,upper=10))
+# user system elapsed
+# 2.75 0.00 2.76
+
#-------------------------------------------------------------------------------
## MSE solution
#-------------------------------------------------------------------------------
@@ -167,11 +170,11 @@
system.time(IC1 <- optIC(model=RobB1, risk=asMSE()))
# user system elapsed
-# 3.62 0.00 3.62
+# 0.41 0.01 0.42
IC1
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -194,7 +197,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
checkIC(IC1)
@@ -216,13 +219,13 @@
#[1] 0.1213661
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -248,7 +251,7 @@
#prob 0.008544305
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -272,7 +275,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -387,13 +390,13 @@
#[1] 0.1213661
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -441,7 +444,7 @@
#prob 0.008544305
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -465,7 +468,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
#
@@ -501,11 +504,11 @@
system.time(IC2 <- optIC(model=RobB2, risk=asMSE()))
# user system elapsed
-# 9.46 0.02 9.47
+# 1.03 0.01 1.05
IC2
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -528,7 +531,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
checkIC(IC2)
@@ -550,13 +553,13 @@
#[1] 0.2239858
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -582,7 +585,7 @@
#prob 0.03342380
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -606,7 +609,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
#
@@ -671,13 +674,13 @@
#[1] 0.2239858
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -703,7 +706,7 @@
#prob 0.03342380
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -727,7 +730,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -738,7 +741,7 @@
#-------------------------------------------------------------------------------
(IC3 <- optIC(model=RobB1, risk=asBias()))
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -780,13 +783,13 @@
#[1] 0.1098910
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -814,7 +817,7 @@
#[1] 0.01011106
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -837,7 +840,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -845,7 +848,7 @@
(IC4 <- optIC(model=RobB2, risk=asBias()))
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -886,13 +889,13 @@
#[1] 0.2159161
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -920,7 +923,7 @@
#[1] 0.01148091
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -943,7 +946,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -956,7 +959,7 @@
(IC5 <- optIC(model=RobB1, risk=asHampel(bound=clip(IC1))))
#minimal bound: 0.1098910
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1001,13 +1004,13 @@
#[1] 0.1213661
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1033,7 +1036,7 @@
#prob 0.008544296
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1057,7 +1060,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -1067,7 +1070,7 @@
(IC6 <- optIC(model=RobB2, risk=asHampel(bound=Risks(IC2)$asBias$value), maxiter = 200))
#minimal bound: 0.2159161
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -1112,13 +1115,13 @@
#[1] 0.2239858
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1144,7 +1147,7 @@
#prob 0.03342372
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1168,7 +1171,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -1181,11 +1184,11 @@
system.time(IC7 <- radiusMinimaxIC(L2Fam=B, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=1))
# user system elapsed
-# 39.39 0.02 39.45
+# 4.55 0.07 4.83
IC7
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1210,7 +1213,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 1]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.391"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.103"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.103"
checkIC(IC7)
@@ -1232,13 +1235,13 @@
#[1] 0.1269559
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1264,7 +1267,7 @@
#prob 0.008272273
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1288,7 +1291,7 @@
#[1] 0.3913008
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.3913008
@@ -1297,11 +1300,11 @@
system.time(IC8 <- radiusMinimaxIC(L2Fam=B, neighbor=TotalVarNeighborhood(),
risk=asMSE(), loRad=0, upRad=1))
# user system elapsed
-# 163.20 0.12 168.21
+# 18.39 0.22 18.67
IC8
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -1326,7 +1329,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 1]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.266"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.146"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.146"
checkIC(IC8)
@@ -1348,13 +1351,13 @@
#[1] 0.2406692
#
#$asBias$biastype
-#An object of class ''symmetricBias''
+#An object of class “symmetricBias”
#Slot "name":
#[1] "symmetric Bias"
#
#
#$asBias$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1380,7 +1383,7 @@
#prob 0.01546324
#
#$trAsCov$normtype
-#An object of class ''NormType''
+#An object of class “NormType”
#Slot "name":
#[1] "EuclideanNorm"
#
@@ -1404,7 +1407,7 @@
#[1] 0.2661058
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.2661058
@@ -1434,7 +1437,7 @@
#current radius: 0.6388282 inefficiency: 1.044571
#current radius: 0.6387762 inefficiency: 1.044584
# user system elapsed
-# 125.41 1.20 143.03
+# 54.50 1.11 55.92
r.rho1
@@ -1464,7 +1467,7 @@
#current radius: 0.3104966 inefficiency: 1.043323
#current radius: 0.3105373 inefficiency: 1.043323
# user system elapsed
-# 431.94 2.64 483.05
+# 280.18 4.97 286.97
r.rho2
#$rho
@@ -1491,7 +1494,7 @@
#Evaluations of Minimum Kolmogorov distance estimate:
#----------------------------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# MDEstimator(x = x, ParamFamily = BinomFamily(size = 25))
#samplesize: 100
@@ -1525,7 +1528,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# oneStepEstimator(x = x, IC = IC9, start = est0)
#samplesize: 100
@@ -1548,7 +1551,7 @@
#asymptotic bias:
#[1] 0.06058743
#(partial) influence curve:
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1571,7 +1574,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 1
@@ -1694,8 +1697,7 @@
# prob
#prob -2.220446e-16
#maximum deviation
-#
- 9.843168e-15
+# 9.843168e-15
## you can also omit step 2
est1v2 <- roptest(x, BinomFamily(size = 25), eps = 0.025,
neighbor = TotalVarNeighborhood(), distance = KolmogorovDist)
@@ -1794,7 +1796,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# kStepEstimator(x = x, IC = IC9, start = est0, steps = 3L)
#samplesize: 100
@@ -1817,7 +1819,7 @@
#asymptotic bias:
#[1] 0.05992703
#(partial) influence curve:
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -1840,7 +1842,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 3
@@ -1946,7 +1948,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# kStepEstimator(x = x, IC = IC10, start = est0, steps = 3L)
#samplesize: 100
@@ -1969,7 +1971,7 @@
#asymptotic bias:
#[1] 0.05982794
#(partial) influence curve:
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -1992,7 +1994,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 3
@@ -2114,7 +2116,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# oneStepEstimator(x = x, IC = IC11, start = est0)
#samplesize: 100
@@ -2137,7 +2139,7 @@
#asymptotic bias:
#[1] 0.07121993
#(partial) influence curve:
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2162,7 +2164,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, Inf]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.601"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.165"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.165"
#steps:
#[1] 1
@@ -2184,7 +2186,7 @@
(est3c1 <- roptest(x, BinomFamily(size = 25), eps.upper = 0.5))
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5)
#samplesize: 100
@@ -2204,7 +2206,7 @@
#asymptotic bias:
#[1] 0.07044238
#(partial) influence curve:
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2229,7 +2231,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 5]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.597"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.159"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.159"
#steps:
#[1] 1
@@ -2246,7 +2248,7 @@
#Evaluations of 1-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5,
# neighbor = TotalVarNeighborhood())
@@ -2267,7 +2269,7 @@
#asymptotic bias:
#[1] 0.07270451
#(partial) influence curve:
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -2292,7 +2294,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, 5]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.31"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.166"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.166"
#steps:
#[1] 1
@@ -2383,7 +2385,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# kStepEstimator(x = x, IC = IC11, start = est0, steps = 3L)
#samplesize: 100
@@ -2406,7 +2408,7 @@
#asymptotic bias:
#[1] 0.07071585
#(partial) influence curve:
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2429,7 +2431,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 3
@@ -2447,7 +2449,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# kStepEstimator(x = x, IC = IC12, start = est0, steps = 3L)
#samplesize: 100
@@ -2470,7 +2472,7 @@
#asymptotic bias:
#[1] 0.07293007
#(partial) influence curve:
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -2493,7 +2495,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 3
@@ -2510,7 +2512,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5,
# steps = 3L)
@@ -2531,7 +2533,7 @@
#asymptotic bias:
#[1] 0.07030448
#(partial) influence curve:
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Binomial family
@@ -2554,7 +2556,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 3
@@ -2571,7 +2573,7 @@
#Evaluations of 3-step estimate:
#-------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# roptest(x = x, L2Fam = BinomFamily(size = 25), eps.upper = 0.5,
# neighbor = TotalVarNeighborhood(), steps = 3L)
@@ -2592,7 +2594,7 @@
#asymptotic bias:
#[1] 0.07266429
#(partial) influence curve:
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Binomial family
@@ -2615,7 +2617,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
#steps:
#[1] 3
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/GammaModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/GammaModel.R 2024-02-06 20:20:56 UTC (rev 1279)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/GammaModel.R 2024-02-06 20:21:54 UTC (rev 1280)
@@ -85,6 +85,8 @@
## MSE solution
system.time(IC1 <- optIC(model=RobG1, risk=asMSE()))
+# user system elapsed
+# 18.78 0.92 19.79
IC1
checkIC(IC1)
Risks(IC1)
@@ -94,7 +96,8 @@
## lower case solutions
system.time(IC2 <- optIC(model=RobG1, risk=asBias(), tol = 1e-10))
-IC2
+# user system elapsed
+# 21.29 1.19 22.47 IC2
checkIC(IC2)
Risks(IC2)
plot(IC2)
@@ -103,6 +106,8 @@
## Hampel solution
system.time(IC3 <- optIC(model=RobG1, risk=asHampel(bound=clip(IC1))))
+# user system elapsed
+# 31.73 2.24 38.50
IC3
checkIC(IC3)
Risks(IC3)
@@ -111,10 +116,13 @@
infoPlot(IC3)
## radius minimax IC
-## takes quite some time - about 180 sec.
+## takes quite some time - about 430 sec.
system.time(IC4 <- radiusMinimaxIC(L2Fam=G, neighbor=ContNeighborhood(),
risk=asMSE(), loRad=0, upRad=Inf))
+# user system elapsed
+# 389.11 22.08 429.66
+
## least favorable radius
## takes really long time - 33 min!
#system.time(r.rho1 <- leastFavorableRadius(L2Fam=G, neighbor=ContNeighborhood(),
@@ -159,3 +167,6 @@
confint(est3, symmetricBias())
confint(est2, symmetricBias())
confint(est4, symmetricBias())
+
+## set back defaults
+distrExOptions(ErelativeTolerance = .Machine$double.eps^0.25) # increase precision for E
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/GumbelLocationModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/GumbelLocationModel.R 2024-02-06 20:20:56 UTC (rev 1279)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/GumbelLocationModel.R 2024-02-06 20:21:54 UTC (rev 1280)
@@ -3,7 +3,16 @@
## computations numerically less stable than in case of the
## Exponential Scale Family
###############################################################################
-require(ROptEst)
+
+###############################################################################
+# NOTE: the infrastructure for the Gumbel Location Family was moved
+# to package RobExtremes (also on CRAN) in version 0.9 (~2013)
+# it remains as a copy in this folder, as it follows the same
+# choreography as the other ones for smoothly parametrized models
+# in this folder
+###############################################################################
+
+if(require(RobExtremes)){
options("newDevice"=TRUE)
## generates Gumbel Location Family with loc = 0
@@ -173,3 +182,4 @@
confint(G0.est21, symmetricBias())
distrExOptions(ElowerTruncQuantile=0) # default
+}
\ No newline at end of file
Modified: branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R
===================================================================
--- branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R 2024-02-06 20:20:56 UTC (rev 1279)
+++ branches/robast-1.3/pkg/ROptEst/inst/scripts/NbinomModel.R 2024-02-06 20:21:54 UTC (rev 1280)
@@ -48,11 +48,11 @@
IC0 <- optIC(model = N, risk = asCov())
IC0 # show IC
-#An object of class ''IC''
+#An object of class “IC”
#### name: Classical optimal influence curve for Negative Binomial family
#### L2-differentiable parametric family: Negative Binomial family
#
-#### 'Curve': An object of class ''EuclRandVarList''
+#### 'Curve': An object of class “EuclRandVarList”
#Domain: Real Space with dimension 1
#[[1]]
#length of Map: 1
@@ -100,7 +100,7 @@
RobN1 <- InfRobModel(center = N, neighbor = ContNeighborhood(radius = 0.5))
RobN1 # show RobN1
-#An object of class ''InfRobModel''
+#An object of class “InfRobModel”
####### center: An object of class "NbinomFamily"
#### name: Negative Binomial family
#
@@ -120,13 +120,13 @@
#### props:
#[1] ""
#
-####### neighborhood: An object of class ''ContNeighborhood''
+####### neighborhood: An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
(RobN2 <- InfRobModel(center = N, neighbor = TotalVarNeighborhood(radius = 0.5)))
-#An object of class ''InfRobModel''
+#An object of class “InfRobModel”
####### center: An object of class "NbinomFamily"
#### name: Negative Binomial family
#
@@ -146,7 +146,7 @@
#### props:
#[1] ""
#
-####### neighborhood: An object of class ''TotalVarNeighborhood''
+####### neighborhood: An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -168,7 +168,7 @@
IC1
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -191,7 +191,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
checkIC(IC1)
@@ -269,7 +269,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -433,7 +433,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
#
@@ -460,7 +460,7 @@
IC2
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -483,7 +483,7 @@
#
#### Infos:
# method message
-#[1,] "optIC" "optimally robust IC for 'asMSE'"
+#[1,] "optIC" "optimally robust IC for ‘asMSE’"
checkIC(IC2)
@@ -561,7 +561,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
#
@@ -684,7 +684,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -697,7 +697,7 @@
(IC3 <- optIC(model=RobN1, risk=asBias()))
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -797,7 +797,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -806,7 +806,7 @@
(IC4 <- optIC(model=RobN2, risk=asBias()))
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -904,7 +904,7 @@
#[1] 0.25
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -918,7 +918,7 @@
(IC5 <- optIC(model=RobN1, risk=asHampel(bound=clip(IC1))))
#minimal bound: 0.05458835
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1020,7 +1020,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5
@@ -1033,7 +1033,7 @@
#minimal bound: 0.1089328
#maximum iterations reached!
# achieved precision: 5.583403e-07
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1136,7 +1136,7 @@
#[1] 0.5
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.5
@@ -1155,7 +1155,7 @@
IC7
-#An object of class ''ContIC''
+#An object of class “ContIC”
#### name: IC of contamination type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1180,7 +1180,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0.01, 3.9]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.581"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.177"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.177"
checkIC(IC7)
@@ -1258,7 +1258,7 @@
#[1] 0.5814856
#
#$asMSE$at
-#An object of class ''ContNeighborhood''
+#An object of class “ContNeighborhood”
#type: (uncond.) convex contamination neighborhood
#radius: 0.5814856
@@ -1272,7 +1272,7 @@
IC8
-#An object of class ''TotalVarIC''
+#An object of class “TotalVarIC”
#### name: IC of total variation type
#
#### L2-differentiable parametric family: Negative Binomial family
@@ -1297,7 +1297,7 @@
# method message
#[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0.01, 1.8]"
#[2,] "radiusMinimaxIC" "least favorable radius: 0.294"
-#[3,] "radiusMinimaxIC" "maximum 'asMSE'-inefficiency: 1.168"
+#[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.168"
checkIC(IC8)
@@ -1377,7 +1377,7 @@
#[1] 0.2944932
#
#$asMSE$at
-#An object of class ''TotalVarNeighborhood''
+#An object of class “TotalVarNeighborhood”
#type: (uncond.) total variation neighborhood
#radius: 0.2944932
@@ -1407,6 +1407,7 @@
#current radius: 0.5625595 inefficiency: 1.044701
# user system elapsed
# 141.37 0.84 150.89
+### 630 sec
## same as for binomial????
@@ -1441,7 +1442,8 @@
#current radius: 0.2866889 inefficiency: 1.044456
# user system elapsed
# 707.48 3.17 760.09
-
+### 630 sec
+
r.rho2
#$rho
@@ -1469,7 +1471,7 @@
#Evaluations of Maximum likelihood estimate:
#-------------------------------------------
-#An object of class ''Estimate''
+#An object of class “Estimate”
#generated by call
# MLEstimator(x = x, ParamFamily = NbinomFamily(size = 25))
#samplesize: 100
@@ -1492,7 +1494,7 @@
#Evaluations of Minimum Kolmogorov distance estimate:
#----------------------------------------------------
-#An object of class ''Estimate''
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/robast -r 1280
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