[Robast-commits] r580 - in branches/robast-0.9/pkg/ROptEst: man tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Jan 31 20:03:44 CET 2013
Author: stamats
Date: 2013-01-31 20:03:44 +0100 (Thu, 31 Jan 2013)
New Revision: 580
Modified:
branches/robast-0.9/pkg/ROptEst/man/cniperCont.Rd
branches/robast-0.9/pkg/ROptEst/man/comparePlot.Rd
branches/robast-0.9/pkg/ROptEst/man/getMaxIneff.Rd
branches/robast-0.9/pkg/ROptEst/man/getReq.Rd
branches/robast-0.9/pkg/ROptEst/man/plot-methods.Rd
branches/robast-0.9/pkg/ROptEst/man/robest.Rd
branches/robast-0.9/pkg/ROptEst/man/roptest.Rd
branches/robast-0.9/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
Log:
update of Rout.save, had to put a lot of examples in \dontrun
Modified: branches/robast-0.9/pkg/ROptEst/man/cniperCont.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/cniperCont.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/cniperCont.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -171,12 +171,15 @@
cniperPointPlot(P, neighbor = ContNeighborhood(radius = 0.5),
risk = asMSE(), lower = 0, upper = 10)
+## Don't run to reduce check time on CRAN
+\dontrun{
## cniper point
cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
risk = asMSE(), lower = 0, upper = 4)
cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
risk = asMSE(), lower = 4, upper = 8)
}
+}
\concept{cniper contamination}
\concept{cniper point}
\keyword{robust}
Modified: branches/robast-0.9/pkg/ROptEst/man/comparePlot.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/comparePlot.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/comparePlot.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -17,12 +17,12 @@
to determine the number of evaluation points.
}
\examples{
-if(require(ROptEst)){
-
N0 <- NormLocationScaleFamily(mean=0, sd=1)
N0.Rob1 <- InfRobModel(center = N0,
neighbor = ContNeighborhood(radius = 0.5))
+## Don't run to reduce check time on CRAN
+\dontrun{
IC1 <- optIC(model = N0, risk = asCov())
IC2 <- optIC(model = N0.Rob1, risk = asMSE())
Modified: branches/robast-0.9/pkg/ROptEst/man/getMaxIneff.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/getMaxIneff.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/getMaxIneff.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -59,11 +59,13 @@
getMaxIneff(N0.ICM,neighbor)
getMaxIneff(N0.ICR,neighbor)
+## Don't run to reduce check time on CRAN
+\dontrun{
N0ls <- NormLocationScaleFamily()
ICsc <- makeIC(list(sin,cos),N0ls)
getMaxIneff(ICsc,neighbor)
-
}
+}
\concept{Inefficiency}
\concept{risk}
\keyword{robust}
Modified: branches/robast-0.9/pkg/ROptEst/man/getReq.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/getReq.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/getReq.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -42,11 +42,15 @@
N0.ICA <- optIC(model = N0.Rob1, risk = asAnscombe(.95))
## MSE solution
N0.ICM <- optIC(model=N0.Rob1, risk=asMSE())
+
+getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=1)
+getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=30)
+
+## Don't run to reduce check time on CRAN
+\dontrun{
## RMX solution
N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
-getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=1)
-getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=30)
getReq(asL1(),neighbor,N0.ICA,N0.ICM,n=30)
getReq(asL4(),neighbor,N0.ICA,N0.ICM,n=30)
getReq(asMSE(),neighbor,N0.ICA,N0.ICR,n=30)
@@ -54,6 +58,7 @@
getReq(asL4(),neighbor,N0.ICA,N0.ICR,n=30)
getReq(asMSE(),neighbor,N0.ICM,N0.ICR,n=30)
+
### when to use MAD and when Qn
## for Qn, see C. Croux, P. Rousseeuw (1993). Alternatives to the Median
## Absolute Deviation, JASA 88(424):1273-1283
@@ -65,6 +70,7 @@
getReq(asMSE(), neighbor, IC.mad, IC.qn, radOrOutl = "Outlier", n = 30)
# => MAD is better once r > 0.5144 (i.e. for more than 2 outliers for n = 30)
}
+}
\concept{Hampel risk}
\concept{risk}
\keyword{robust}
Modified: branches/robast-0.9/pkg/ROptEst/man/plot-methods.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/plot-methods.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/plot-methods.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -17,10 +17,13 @@
\examples{
N <- NormLocationScaleFamily(mean=0, sd=1)
IC <- optIC(model = N, risk = asCov())
+## Don't run to reduce check time on CRAN
+\dontrun{
plot(IC, main = TRUE, panel.first= grid(),
col = "blue", cex.main = 2, cex.inner = 0.6,
withMBR=TRUE)
}
+}
\keyword{methods}
\keyword{distribution}
Modified: branches/robast-0.9/pkg/ROptEst/man/robest.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/robest.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/robest.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -58,6 +58,8 @@
\code{\link[RobAStBase]{UncondNeighborhood-class}},
\code{\link[distrMod]{RiskType-class}} }
\examples{
+## Don't run to reduce check time on CRAN
+\dontrun{
#############################
## 1. Binomial data
#############################
@@ -76,6 +78,7 @@
nb <- gennbCtrl(eps=0.05)
robest1 <- robest(x, BF, nbCtrl = nb, steps = 3)
estimate(robest1)
+
confint(robest1, method = symmetricBias())
## neglecting bias
confint(robest1)
@@ -127,6 +130,7 @@
nb1 <- gennbCtrl(eps.upper = 0.1)
robest <- robest(x, PF, nbCtrl = nb1, steps = 3)
estimate(robest)
+
confint(robest, symmetricBias())
plot(pIC(robest))
tmp <- qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
@@ -138,8 +142,8 @@
estimate(robest1)
confint(robest1, symmetricBias())
plot(pIC(robest1))
+}
-
#############################
## 3. Normal (Gaussian) location and scale
#############################
@@ -155,6 +159,8 @@
estimate(MLest)
confint(MLest)
+## Don't run to reduce check time on CRAN
+\dontrun{
## compute optimally robust estimator (known contamination)
## takes some time -> you can use package RobLox for normal
## location and scale which is optimized for speed
@@ -163,6 +169,7 @@
estimate.call(robEst)
attr(robEst,"timings")
estimate(robest)
+
confint(robest, symmetricBias())
plot(pIC(robest))
## plot of relative and absolute information; cf. Kohl (2005)
@@ -173,17 +180,21 @@
exp.cex2.lbl = .12,exp.fadcol.lbl = .45,
nosym.pCI = TRUE, adj.lbl=c(1.7,.2),
exact.pCI = FALSE, log ="xy")
-
+}
+
## finite-sample correction
if(require(RobLox)){
n <- length(chem)
r <- 0.05*sqrt(n)
r.fi <- finiteSampleCorrection(n = n, r = r)
fsCor0 <- r.fi/r
+ nb1 <- gennbCtrl(eps = 0.05)
robest <- robest(chem, NF, nbCtrl = nb1, fsCor = fsCor0, steps = 3)
estimate(robest)
}
+## Don't run to reduce check time on CRAN
+\dontrun{
## compute optimally robust estimator (unknown contamination)
## takes some time -> use package RobLox!
nb2 <- gennbCtrl(eps.lower = 0.05, eps.upper = 0.1)
@@ -194,6 +205,7 @@
## plot of relative and absolute information; cf. Kohl (2005)
infoPlot(pIC(robest1))
}
+}
\concept{k-step construction}
\concept{optimally robust estimation}
\keyword{robust}
Modified: branches/robast-0.9/pkg/ROptEst/man/roptest.Rd
===================================================================
--- branches/robast-0.9/pkg/ROptEst/man/roptest.Rd 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/man/roptest.Rd 2013-01-31 19:03:44 UTC (rev 580)
@@ -158,7 +158,7 @@
Infinitesimally Robust Estimation in General Smoothly Parametrized Models.
\emph{Stat. Methods Appl.}, \bold{19}, 333--354.
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
+ Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing
the Radius. Statistical Methods and Applications \bold{17}(1) 13-40.
@@ -177,6 +177,8 @@
\code{\link[RobAStBase]{UncondNeighborhood-class}},
\code{\link[distrMod]{RiskType-class}} }
\examples{
+## Don't run to reduce check time on CRAN
+\dontrun{
#############################
## 1. Binomial data
#############################
@@ -221,7 +223,6 @@
confint(robest4, method = symmetricBias())
plot(pIC(robest4))
-
#############################
## 2. Poisson data
#############################
@@ -239,6 +240,7 @@
robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
estimate(robest)
confint(robest, symmetricBias())
+
plot(pIC(robest))
tmp <- qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
exp.fadcol.pch = .55, jit.fac=.9)
@@ -249,8 +251,8 @@
estimate(robest1)
confint(robest1, symmetricBias())
plot(pIC(robest1))
+}
-
#############################
## 3. Normal (Gaussian) location and scale
#############################
@@ -264,6 +266,8 @@
estimate(MLest)
confint(MLest)
+## Don't run to reduce check time on CRAN
+\dontrun{
## compute optimally robust estimator (known contamination)
## takes some time -> you can use package RobLox for normal
## location and scale which is optimized for speed
@@ -279,6 +283,7 @@
exp.cex2.lbl = .12,exp.fadcol.lbl = .45,
nosym.pCI = TRUE, adj.lbl=c(1.7,.2),
exact.pCI = FALSE, log ="xy")
+}
## finite-sample correction
if(require(RobLox)){
@@ -291,6 +296,8 @@
estimate(robest)
}
+## Don't run to reduce check time on CRAN
+\dontrun{
## compute optimally robust estimator (unknown contamination)
## takes some time -> use package RobLox!
robest1 <- roptest(chem, NormLocationScaleFamily(), eps.lower = 0.05,
@@ -301,6 +308,7 @@
## plot of relative and absolute information; cf. Kohl (2005)
infoPlot(pIC(robest1))
}
+}
\concept{k-step construction}
\concept{optimally robust estimation}
\keyword{robust}
Modified: branches/robast-0.9/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
===================================================================
--- branches/robast-0.9/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save 2013-01-31 12:51:28 UTC (rev 579)
+++ branches/robast-0.9/pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save 2013-01-31 19:03:44 UTC (rev 580)
@@ -1,6 +1,6 @@
-R version 2.15.1 Patched (2012-06-29 r59688) -- "Roasted Marshmallows"
-Copyright (C) 2012 The R Foundation for Statistical Computing
+R Under development (unstable) (2013-01-26 r61749) -- "Unsuffered Consequences"
+Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-unknown-linux-gnu (64-bit)
@@ -34,6 +34,13 @@
:SweaveListingUtils> Utilities for Sweave together with
:SweaveListingUtils> TeX listings package (version 0.6)
:SweaveListingUtils>
+:SweaveListingUtils> NOTE: Support for this package
+:SweaveListingUtils> will stop soon.
+:SweaveListingUtils>
+:SweaveListingUtils> Package 'knitr' is providing the
+:SweaveListingUtils> same functionality in a better
+:SweaveListingUtils> way.
+:SweaveListingUtils>
:SweaveListingUtils> Some functions from package 'base'
:SweaveListingUtils> are intentionally masked ---see
:SweaveListingUtils> SweaveListingMASK().
@@ -53,7 +60,7 @@
Attaching package: ‘SweaveListingUtils’
-The following object(s) are masked from ‘package:base’:
+The following object is masked from ‘package:base’:
library, require
@@ -79,7 +86,7 @@
Attaching package: ‘distr’
-The following object(s) are masked from ‘package:stats’:
+The following object is masked from ‘package:stats’:
df, qqplot, sd
@@ -103,7 +110,7 @@
Attaching package: ‘distrEx’
-The following object(s) are masked from ‘package:stats’:
+The following object is masked from ‘package:stats’:
IQR, mad, median, var
@@ -141,19 +148,25 @@
Attaching package: ‘distrMod’
-The following object(s) are masked from ‘package:stats4’:
+The following object is masked from ‘package:stats4’:
confint
-The following object(s) are masked from ‘package:stats’:
+The following object is masked from ‘package:stats’:
confint
-The following object(s) are masked from ‘package:base’:
+The following object is masked from ‘package:base’:
norm
Loading required package: RobAStBase
+Loading required package: rrcov
+Loading required package: robustbase
+Loading required package: pcaPP
+Loading required package: mvtnorm
+Scalable Robust Estimators with High Breakdown Point (version 1.3-02)
+
:RobAStBase> Robust Asymptotic Statistics (version 0.9)
:RobAStBase>
:RobAStBase> Some functions from pkg's 'stats' and 'graphics'
@@ -169,18 +182,29 @@
Attaching package: ‘RobAStBase’
-The following object(s) are masked from ‘package:graphics’:
+The following object is masked from ‘package:graphics’:
clip
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
+> assign(".ExTimings", "ROptEst-Ex.timings", pos = 'CheckExEnv')
+> cat("name\tuser\tsystem\telapsed\n", file=get(".ExTimings", pos = 'CheckExEnv'))
+> assign(".format_ptime",
++ function(x) {
++ if(!is.na(x[4L])) x[1L] <- x[1L] + x[4L]
++ if(!is.na(x[5L])) x[2L] <- x[2L] + x[5L]
++ format(x[1L:3L])
++ },
++ pos = 'CheckExEnv')
+>
> cleanEx()
> nameEx("0ROptEst-package")
> ### * 0ROptEst-package
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ROptEst-package
> ### Title: Optimally robust estimation
> ### Aliases: ROptEst-package ROptEst
@@ -224,12 +248,35 @@
MLEstimator(x = x, ParamFamily = PoisFamily())
>
> ## compute optimally (w.r.t to MSE) robust estimator (unknown contamination)
-> robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
-> estimate(robest)
+> robEst <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
+> estimate(robEst)
lambda
3.908135
> ## check influence curve
-> checkIC(pIC(robest))
+> pIC(robEst)
+An object of class “ContIC”
+### name: IC of contamination type
+
+### L2-differentiable parametric family: Poisson family
+### param: An object of class "ParamFamParameter"
+name: positive mean
+lambda: 3.90883569611022
+trafo:
+ lambda
+lambda 1
+
+### neighborhood radius: 0.560986
+
+### clip: [1] 2.751564
+### cent: [1] -0.2472607
+### stand:
+ lambda
+lambda 6.943887
+
+### Infos:
+ method message
+[1,] "optIC" "optimally robust IC for ‘asMSE’"
+> checkIC(pIC(robEst))
precision of centering: -2.707017e-08
precision of Fisher consistency:
lambda
@@ -237,9 +284,10 @@
maximum deviation
1.980378e-06
> ## plot influence curve
-> plot(pIC(robest))
+> plot(pIC(robEst))
+NULL
> ## confidence interval based on LAN - neglecting bias
-> confint(robest)
+> confint(robEst)
A[n] asymptotic (LAN-based) confidence interval:
2.5 % 97.5 %
lambda 3.826169 3.990102
@@ -248,7 +296,7 @@
Call by which estimate was produced:
roptest(x = x, L2Fam = PoisFamily(), eps.upper = 0.1, steps = 3)
> ## confidence interval based on LAN - including bias
-> confint(robest, method = symmetricBias())
+> confint(robEst, method = symmetricBias())
A[n] asymptotic (LAN-based), uniform (bias-aware)
confidence interval:
for symmetric Bias
@@ -261,12 +309,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("0ROptEst-package", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("asAnscombe-class")
> ### * asAnscombe-class
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: asAnscombe-class
> ### Title: Asymptotic Anscombe risk
> ### Aliases: asAnscombe-class eff eff,asAnscombe-method
@@ -282,12 +334,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("asAnscombe-class", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("asAnscombe")
> ### * asAnscombe
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: asAnscombe
> ### Title: Generating function for asAnscombe-class
> ### Aliases: asAnscombe
@@ -310,12 +366,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("asAnscombe", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("asL1-class")
> ### * asL1-class
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: asL1-class
> ### Title: Asymptotic mean absolute error
> ### Aliases: asL1-class
@@ -329,12 +389,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("asL1-class", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("asL1")
> ### * asL1
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: asL1
> ### Title: Generating function for asMSE-class
> ### Aliases: asL1
@@ -356,12 +420,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("asL1", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("asL4-class")
> ### * asL4-class
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: asL4-class
> ### Title: Asymptotic mean power 4 error
> ### Aliases: asL4-class
@@ -375,12 +443,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("asL4-class", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("asL4")
> ### * asL4
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: asL4
> ### Title: Generating function for asL4-class
> ### Aliases: asL4
@@ -402,21 +474,20 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("asL4", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("cniperCont")
> ### * cniperCont
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: cniperCont
-> ### Title: Generic Functions for Computation and Plot of Cniper
-> ### Contamination and Cniper Points.
-> ### Aliases: cniperCont cniperCont-methods
-> ### cniperCont,IC,IC,L2ParamFamily,ContNeighborhood,asMSE-method
-> ### cniperPoint cniperPoint-methods
-> ### cniperPoint,L2ParamFamily,ContNeighborhood,asMSE-method
-> ### cniperPointPlot cniperPointPlot-methods
-> ### cniperPointPlot,L2ParamFamily,ContNeighborhood,asMSE-method
+> ### Title: Functions for Computation and Plot of Cniper Contamination and
+> ### Cniper Points.
+> ### Aliases: cniperCont cniperPoint cniperPointPlot
> ### Keywords: robust
>
> ### ** Examples
@@ -427,7 +498,7 @@
> IC1 <- optIC(model=RobP1, risk=asMSE())
> RobP2 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 1))
> IC2 <- optIC(model=RobP2, risk=asMSE())
-> cniperCont(IC1 = IC1, IC2 = IC2, L2Fam = P,
+> cniperCont(IC1 = IC1, IC2 = IC2,
+ neighbor = ContNeighborhood(radius = 0.5),
+ risk = asMSE(),
+ lower = 0, upper = 8, n = 101)
@@ -436,24 +507,58 @@
> cniperPointPlot(P, neighbor = ContNeighborhood(radius = 0.5),
+ risk = asMSE(), lower = 0, upper = 10)
>
-> ## cniper point
-> cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
-+ risk = asMSE(), lower = 0, upper = 4)
-cniper point
- 0.7803439
-> cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
-+ risk = asMSE(), lower = 4, upper = 8)
-cniper point
- 7.219656
+> ## Don't run to reduce check time on CRAN
+> ## Not run:
+> ##D ## cniper point
+> ##D cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
+> ##D risk = asMSE(), lower = 0, upper = 4)
+> ##D cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
+> ##D risk = asMSE(), lower = 4, upper = 8)
+> ## End(Not run)
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("cniperCont", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
+> nameEx("comparePlot")
+> ### * comparePlot
+>
+> flush(stderr()); flush(stdout())
+>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
+> ### Name: comparePlot-methods
+> ### Title: Compare - Plots
+> ### Aliases: comparePlot comparePlot-methods comparePlot,IC,IC-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> N0 <- NormLocationScaleFamily(mean=0, sd=1)
+> N0.Rob1 <- InfRobModel(center = N0,
++ neighbor = ContNeighborhood(radius = 0.5))
+>
+> ## Don't run to reduce check time on CRAN
+> ## Not run:
+> ##D IC1 <- optIC(model = N0, risk = asCov())
+> ##D IC2 <- optIC(model = N0.Rob1, risk = asMSE())
+> ##D
+> ##D comparePlot(IC1,IC2, withMBR=TRUE)
+> ## End(Not run)
+>
+>
+>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("comparePlot", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
+> cleanEx()
> nameEx("getL1normL2deriv")
> ### * getL1normL2deriv
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: getL1normL2deriv
> ### Title: Calculation of L1 norm of L2derivative
> ### Aliases: getL1normL2deriv getL1normL2deriv-methods
@@ -467,12 +572,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getL1normL2deriv", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("getL2normL2deriv")
> ### * getL2normL2deriv
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: getL2normL2deriv
> ### Title: Calculation of L2 norm of L2derivative
> ### Aliases: getL2normL2deriv
@@ -484,12 +593,16 @@
>
>
>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getL2normL2deriv", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
> nameEx("getMaxIneff")
> ### * getMaxIneff
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: getMaxIneff
> ### Title: getMaxIneff - computation of the maximal inefficiency of an IC
> ### Aliases: getMaxIneff
@@ -541,25 +654,88 @@
> getMaxIneff(N0.ICR,neighbor)
[1] 1.180746
>
-> N0ls <- NormLocationScaleFamily()
-> ICsc <- makeIC(list(sin,cos),N0ls)
-> getMaxIneff(ICsc,neighbor)
-Warning in A[DA.comp] <- matrix(param[1:lA.comp], ncol = k, nrow = p) :
- number of items to replace is not a multiple of replacement length
-[1] 2.679436
+> ## Don't run to reduce check time on CRAN
+> ## Not run:
+> ##D N0ls <- NormLocationScaleFamily()
+> ##D ICsc <- makeIC(list(sin,cos),N0ls)
+> ##D getMaxIneff(ICsc,neighbor)
+> ## End(Not run)
>
>
>
>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getMaxIneff", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
> cleanEx()
+> nameEx("getRadius")
+> ### * getRadius
+>
+> flush(stderr()); flush(stdout())
+>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
+> ### Name: getRadius
+> ### Title: Computation of the Optimal Radius for Given Clipping Bound
+> ### Aliases: getRadius
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> N <- NormLocationFamily(mean=0, sd=1)
+> nb <- ContNeighborhood(); ri <- asMSE()
+> radIC <- radiusMinimaxIC(L2Fam=N, neighbor=nb, risk=ri, loRad=0.1, upRad=0.5)
+> getRadius(radIC, L2Fam=N, neighbor=nb, risk=ri)
+[1] 0.2853168
+>
+> ## taken from script NormalScaleModel.R in folder scripts
+> N0 <- NormScaleFamily(mean=0, sd=1)
+> (N0.IC7 <- radiusMinimaxIC(L2Fam=N0, neighbor=nb, risk=ri, loRad=0, upRad=Inf))
+An object of class “ContIC”
+### name: IC of contamination type
+
+### L2-differentiable parametric family: normal scale family
+### param: An object of class "ParamWithScaleFamParameter"
+name: scale
+sd: 1
+fixed part of param.:
+ mean: 0
+trafo:
+ scale
+scale 1
+
+### neighborhood radius: 0.4989504
+
+### clip: [1] 1.430943
+### cent: [1] -0.3562353
+### stand:
+ scale
+scale 1.26182
+
+### Infos:
+ method message
+[1,] "radiusMinimaxIC" "radius minimax IC for radius interval [0, Inf]"
+[2,] "radiusMinimaxIC" "least favorable radius: 0.499"
+[3,] "radiusMinimaxIC" "maximum ‘asMSE’-inefficiency: 1.504"
+> ##
+> getRadius(N0.IC7, risk=asMSE(), neighbor=nb, L2Fam=N0)
+[1] 0.4989504
+> getRadius(N0.IC7, risk=asL4(), neighbor=nb, L2Fam=N0)
+[1] 0.6127672
+>
+>
+>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getRadius", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
+> cleanEx()
> nameEx("getReq")
> ### * getReq
>
> flush(stderr()); flush(stdout())
>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: getReq
> ### Title: getReq - computation of the radius interval where IC1 is better
-> ### than IC2
+> ### than IC2.
> ### Aliases: getReq
> ### Keywords: robust
>
@@ -596,8 +772,6 @@
achieved precision: 0.04096877
> ## MSE solution
> N0.ICM <- optIC(model=N0.Rob1, risk=asMSE())
-> ## RMX solution
-> N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
>
> getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=1)
Warning in .local(IC, risk, L2Fam, ...) :
@@ -609,97 +783,193 @@
The maximum deviation from the exact IC properties is 0.0020208733776802
This is larger than the specified 'tol' => the result may be wrong
[1] 0.00000000 0.06848038
-> getReq(asL1(),neighbor,N0.ICA,N0.ICM,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.06544434
-> getReq(asL4(),neighbor,N0.ICA,N0.ICM,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.07542161
-> getReq(asMSE(),neighbor,N0.ICA,N0.ICR,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.07544307
-> getReq(asL1(),neighbor,N0.ICA,N0.ICR,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.07161849
-> getReq(asL4(),neighbor,N0.ICA,N0.ICR,n=30)
-Warning in .local(IC, risk, L2Fam, ...) :
- The maximum deviation from the exact IC properties is 0.0020208733776802
-This is larger than the specified 'tol' => the result may be wrong
-[1] 0.00000000 0.08429762
-> getReq(asMSE(),neighbor,N0.ICM,N0.ICR,n=30)
-[1] 0.0000000 0.1016517
>
-> ### when to use MAD and when Qn
-> ## for Qn, see C. Croux, P. Rousseeuw (1993). Alternatives to the Median
-> ## Absolute Deviation, JASA 88(424):1273-1283
-> L2M <- NormScaleFamily()
-> IC.mad <- makeIC(function(x)sign(abs(x)-qnorm(.75)),L2M)
-$Curve
-An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
+> ## Don't run to reduce check time on CRAN
+> ## Not run:
+> ##D ## RMX solution
+> ##D N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
+> ##D
+> ##D getReq(asL1(),neighbor,N0.ICA,N0.ICM,n=30)
+> ##D getReq(asL4(),neighbor,N0.ICA,N0.ICM,n=30)
+> ##D getReq(asMSE(),neighbor,N0.ICA,N0.ICR,n=30)
+> ##D getReq(asL1(),neighbor,N0.ICA,N0.ICR,n=30)
+> ##D getReq(asL4(),neighbor,N0.ICA,N0.ICR,n=30)
+> ##D getReq(asMSE(),neighbor,N0.ICM,N0.ICR,n=30)
+> ##D
+> ##D
+> ##D ### when to use MAD and when Qn
+> ##D ## for Qn, see C. Croux, P. Rousseeuw (1993). Alternatives to the Median
+> ##D ## Absolute Deviation, JASA 88(424):1273-1283
+> ##D L2M <- NormScaleFamily()
+> ##D IC.mad <- makeIC(function(x)sign(abs(x)-qnorm(.75)),L2M)
+> ##D d.qn <- (2^.5*qnorm(5/8))^-1
+> ##D IC.qn <- makeIC(function(x) d.qn*(1/4 - pnorm(x+1/d.qn) + pnorm(x-1/d.qn)), L2M)
+> ##D getReq(asMSE(), neighbor, IC.mad, IC.qn)
+> ##D getReq(asMSE(), neighbor, IC.mad, IC.qn, radOrOutl = "Outlier", n = 30)
+> ##D # => MAD is better once r > 0.5144 (i.e. for more than 2 outliers for n = 30)
+> ## End(Not run)
+>
+>
+>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getReq", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
+> cleanEx()
+> nameEx("getRiskFctBV-methods")
+> ### * getRiskFctBV-methods
+>
+> flush(stderr()); flush(stdout())
+>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
+> ### Name: getRiskFctBV-methods
+> ### Title: Methods for Function getRiskFctBV in Package 'ROptEst'
+> ### Aliases: getRiskFctBV getRiskFctBV-methods getRiskFctBV,asL1,ANY-method
+> ### getRiskFctBV,asL4,ANY-method
+> ### Keywords: classes
+>
+> ### ** Examples
+>
+> myrisk <- asMSE()
+> getRiskFctBV(myrisk)
+function (bias, var)
+bias^2 + var
+<environment: 0xb596930>
+>
+>
+>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getRiskFctBV-methods", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
+> cleanEx()
+> nameEx("getRiskIC")
+> ### * getRiskIC
+>
+> flush(stderr()); flush(stdout())
+>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
+> ### Name: getRiskIC
+> ### Title: Generic function for the computation of a risk for an IC
+> ### Aliases: getRiskIC getRiskIC-methods
+> ### getRiskIC,HampIC,asCov,missing,missing-method
+> ### getRiskIC,HampIC,asCov,missing,L2ParamFamily-method
+> ### getRiskIC,TotalVarIC,asCov,missing,L2ParamFamily-method
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> B <- BinomFamily(size = 25, prob = 0.25)
+>
+> ## classical optimal IC
+> IC0 <- optIC(model = B, risk = asCov())
+> getRiskIC(IC0, asCov())
+$asCov
+$asCov$distribution
+[1] "Binom(25, 0.25)"
-$CallL2Fam
-L2Fam at fam.call
+$asCov$value
+ [,1]
+[1,] 0.0075
-An object of class “IC”
-### name: square integrable (partial) influence curve
-### L2-differentiable parametric family: normal scale family
-### 'Curve': An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
+>
+>
+>
+>
+> assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
+> cat("getRiskIC", get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
+> cleanEx()
+> nameEx("inputGenerator")
+> ### * inputGenerator
+>
+> flush(stderr()); flush(stdout())
+>
+> assign(".ptime", proc.time(), pos = "CheckExEnv")
+> ### Name: inputGenerators
+> ### Title: Input generating functions for function 'robest'
+> ### Aliases: inputGenerators gennbCtrl genstartCtrl genkStepCtrl
+> ### Keywords: robust
+>
+> ### ** Examples
+>
+> genkStepCtrl()
+$useLast
+[1] FALSE
-### Infos:
- method message
-> d.qn <- (2^.5*qnorm(5/8))^-1
-> IC.qn <- makeIC(function(x) d.qn*(1/4 - pnorm(x+1/d.qn) + pnorm(x-1/d.qn)), L2M)
-$Curve
-An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
+$withUpdateInKer
+[1] FALSE
-$CallL2Fam
-L2Fam at fam.call
+$IC.UpdateInKer
+getRobAStBaseOption("IC.UpdateInKer")
-An object of class “IC”
-### name: square integrable (partial) influence curve
-### L2-differentiable parametric family: normal scale family
+$withICList
+[1] FALSE
-### 'Curve': An object of class “EuclRandVarList”
-Domain: Real Space with dimension 1
-[[1]]
-length of Map: 1
-Range: Real Space with dimension 1
+$withPICList
+[1] FALSE
-### Infos:
- method message
-> getReq(asMSE(), neighbor, IC.mad, IC.qn)
-[1] 0.5074459 Inf
-> # => MAD is better once r > 0.5144 (i.e. for more than 2 outliers for n = 30)
+$scalename
+[1] "scale"
+
+$withLogScale
+[1] TRUE
+
+> genstartCtrl()
+$distance
+function (e1, e2, ...)
+standardGeneric("CvMDist")
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/robast -r 580
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