[Robast-commits] r444 - in pkg/ROptEst: . man tests/Examples

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Wed Jan 5 20:09:20 CET 2011


Author: stamats
Date: 2011-01-05 20:09:20 +0100 (Wed, 05 Jan 2011)
New Revision: 444

Modified:
   pkg/ROptEst/DESCRIPTION
   pkg/ROptEst/man/roptest.Rd
   pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
Log:
updated ROptEst-Ex.Rout.save files to R 2.12.1 patched, added 'Suggests: MASS, RobLox' to DESCRIPTION file, minor modification of roptest examples

Modified: pkg/ROptEst/DESCRIPTION
===================================================================
--- pkg/ROptEst/DESCRIPTION	2011-01-05 18:34:04 UTC (rev 443)
+++ pkg/ROptEst/DESCRIPTION	2011-01-05 19:09:20 UTC (rev 444)
@@ -6,6 +6,7 @@
         classes and methods.
 Depends: R(>= 2.7.0), methods, distr(>= 2.0), distrEx(>= 2.0), distrMod(>= 2.0), RandVar(>=
         0.6.4), RobAStBase
+Suggests: MASS, RobLox
 Author: Matthias Kohl, Peter Ruckdeschel
 Maintainer: Matthias Kohl <Matthias.Kohl at stamats.de>
 LazyLoad: yes

Modified: pkg/ROptEst/man/roptest.Rd
===================================================================
--- pkg/ROptEst/man/roptest.Rd	2011-01-05 18:34:04 UTC (rev 443)
+++ pkg/ROptEst/man/roptest.Rd	2011-01-05 19:09:20 UTC (rev 444)
@@ -169,8 +169,9 @@
 ## neglecting bias
 confint(robest1)
 plot(pIC(robest1))
-qqplot(x, robest1, cex.pch=1.5, exp.cex2.pch = -.25,
-       exp.fadcol.pch = .55, jit.fac=.9)
+qq1 <- qqplot(x, robest1, cex.pch=1.5, exp.cex2.pch = -.25,
+              exp.fadcol.pch = .55, jit.fac=.9)
+str(qq1)
 
 ## compute optimally robust estimator (unknown contamination)
 robest2 <- roptest(x, BinomFamily(size = 25), eps.lower = 0, eps.upper = 0.2, steps = 3)
@@ -211,8 +212,9 @@
 estimate(robest)
 confint(robest, symmetricBias())
 plot(pIC(robest))
-qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
-       exp.fadcol.pch = .55, jit.fac=.9)
+qq2 <- qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
+              exp.fadcol.pch = .55, jit.fac=.9)
+str(qq2)
  
 ## total variation neighborhoods (unknown deviation)
 robest1 <- roptest(x, PoisFamily(), eps.upper = 0.05, 
@@ -245,11 +247,12 @@
 ## plot of relative and absolute information; cf. Kohl (2005)
 infoPlot(pIC(robest))
 
-qqplot(chem, robest, cex.pch=1.5, exp.cex2.pch = -.25,
-       exp.fadcol.pch = .55, withLab = TRUE, which.Order=1:4,
-       exp.cex2.lbl = .12,exp.fadcol.lbl = .45,
-       nosym.pCI = TRUE, adj.lbl=c(1.7,.2),
-       exact.pCI = FALSE, log ="xy")
+qq3 <- qqplot(chem, robest, cex.pch=1.5, exp.cex2.pch = -.25,
+              exp.fadcol.pch = .55, withLab = TRUE, which.Order=1:4,
+              exp.cex2.lbl = .12,exp.fadcol.lbl = .45,
+              nosym.pCI = TRUE, adj.lbl=c(1.7,.2),
+              exact.pCI = FALSE, log ="xy")
+str(qq3)
 
 ## finite-sample correction
 if(require(RobLox)){

Modified: pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save
===================================================================
--- pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save	2011-01-05 18:34:04 UTC (rev 443)
+++ pkg/ROptEst/tests/Examples/ROptEst-Ex.Rout.save	2011-01-05 19:09:20 UTC (rev 444)
@@ -1,7 +1,8 @@
 
-R version 2.10.0 beta (2009-10-15 r50107)
-Copyright (C) 2009 The R Foundation for Statistical Computing
+R version 2.12.1 Patched (2011-01-04 r53913)
+Copyright (C) 2011 The R Foundation for Statistical Computing
 ISBN 3-900051-07-0
+Platform: x86_64-unknown-linux-gnu (64-bit)
 
 R is free software and comes with ABSOLUTELY NO WARRANTY.
 You are welcome to redistribute it under certain conditions.
@@ -17,78 +18,13 @@
 'help.start()' for an HTML browser interface to help.
 Type 'q()' to quit R.
 
-> ### * <HEADER>
-> ###
-> attach(NULL, name = "CheckExEnv")
-> assign("nameEx",
-+        local({
-+ 	   s <- "__{must remake R-ex/*.R}__"
-+            function(new) {
-+                if(!missing(new)) s <<- new else s
-+            }
-+        }),
-+        pos = "CheckExEnv")
-> ## Add some hooks to label plot pages for base and grid graphics
-> assign("base_plot_hook",
-+        function() {
-+            pp <- par(c("mfg","mfcol","oma","mar"))
-+            if(all(pp$mfg[1:2] == c(1, pp$mfcol[2]))) {
-+                outer <- (oma4 <- pp$oma[4]) > 0; mar4 <- pp$mar[4]
-+                mtext(sprintf("help(\"%s\")", nameEx()), side = 4,
-+                      line = if(outer)max(1, oma4 - 1) else min(1, mar4 - 1),
-+                outer = outer, adj = 1, cex = .8, col = "orchid", las=3)
-+            }
-+        },
-+        pos = "CheckExEnv")
-> assign("grid_plot_hook",
-+        function() {
-+            grid::pushViewport(grid::viewport(width=grid::unit(1, "npc") -
-+                               grid::unit(1, "lines"), x=0, just="left"))
-+            grid::grid.text(sprintf("help(\"%s\")", nameEx()),
-+                            x=grid::unit(1, "npc") + grid::unit(0.5, "lines"),
-+                            y=grid::unit(0.8, "npc"), rot=90,
-+                            gp=grid::gpar(col="orchid"))
-+        },
-+        pos = "CheckExEnv")
-> setHook("plot.new",     get("base_plot_hook", pos = "CheckExEnv"))
-> setHook("persp",        get("base_plot_hook", pos = "CheckExEnv"))
-> setHook("grid.newpage", get("grid_plot_hook", pos = "CheckExEnv"))
-> assign("cleanEx",
-+        function(env = .GlobalEnv) {
-+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
-+            RNGkind("default", "default")
-+ 	   set.seed(1)
-+    	   options(warn = 1)
-+ 	   .CheckExEnv <- as.environment("CheckExEnv")
-+ 	   delayedAssign("T", stop("T used instead of TRUE"),
-+ 		  assign.env = .CheckExEnv)
-+ 	   delayedAssign("F", stop("F used instead of FALSE"),
-+ 		  assign.env = .CheckExEnv)
-+ 	   sch <- search()
-+ 	   newitems <- sch[! sch %in% .oldSearch]
-+ 	   for(item in rev(newitems))
-+                eval(substitute(detach(item), list(item=item)))
-+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
-+ 	   if(length(missitems))
-+ 	       warning("items ", paste(missitems, collapse=", "),
-+ 		       " have been removed from the search path")
-+        },
-+        pos = "CheckExEnv")
-> assign("ptime", proc.time(), pos = "CheckExEnv")
-> ## at least one package changes these via ps.options(), so do this
-> ## before loading the package.
-> ## Use postscript as incomplete files may be viewable, unlike PDF.
-> ## Choose a size that is close to on-screen devices, fix paper
-> grDevices::ps.options(width = 7, height = 7, paper = "a4", reset = TRUE)
-> grDevices::postscript("ROptEst-Ex.ps")
-> 
-> assign("par.postscript", graphics::par(no.readonly = TRUE), pos = "CheckExEnv")
-> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
+> pkgname <- "ROptEst"
+> source(file.path(R.home("share"), "R", "examples-header.R"))
 > options(warn = 1)
 > library('ROptEst')
 Loading required package: distr
 Loading required package: startupmsg
-:startupmsg>  Utilities for start-up messages (version 0.7)
+:startupmsg>  Utilities for start-up messages (version 0.7.1)
 :startupmsg> 
 :startupmsg>  For more information see ?"startupmsg",
 :startupmsg>  NEWS("startupmsg")
@@ -96,7 +32,7 @@
 Loading required package: sfsmisc
 Loading required package: SweaveListingUtils
 :SweaveListingUtils>  Utilities for Sweave together with
-:SweaveListingUtils>  TeX listings package (version 0.4)
+:SweaveListingUtils>  TeX listings package (version 0.5)
 :SweaveListingUtils> 
 :SweaveListingUtils>  Some functions from package 'base'
 :SweaveListingUtils>  are intentionally masked ---see
@@ -117,14 +53,12 @@
 
 Attaching package: 'SweaveListingUtils'
 
+The following object(s) are masked from 'package:base':
 
-	The following object(s) are masked from package:base :
+    library, require
 
-	 library,
-	 require 
-
-:distr>  Object orientated implementation of distributions (version
-:distr>  2.2)
+:distr>  Object oriented implementation of distributions (version
+:distr>  2.3)
 :distr> 
 :distr>  Attention: Arithmetics on distribution objects are
 :distr>  understood as operations on corresponding random variables
@@ -145,25 +79,21 @@
 
 Attaching package: 'distr'
 
+The following object(s) are masked from 'package:stats':
 
-	The following object(s) are masked from package:stats :
+    df, qqplot, sd
 
-	 df,
-	 qqplot,
-	 sd 
-
 Loading required package: distrEx
 Loading required package: evd
 Loading required package: actuar
 
 Attaching package: 'actuar'
 
+The following object(s) are masked from 'package:grDevices':
 
-	The following object(s) are masked from package:grDevices :
+    cm
 
-	 cm 
-
-:distrEx>  Extensions of package distr (version 2.2)
+:distrEx>  Extensions of package distr (version 2.3)
 :distrEx> 
 :distrEx>  Note: Packages "e1071", "moments", "fBasics" should be
 :distrEx>  attached /before/ package "distrEx". See distrExMASK().
@@ -178,17 +108,13 @@
 
 Attaching package: 'distrEx'
 
+The following object(s) are masked from 'package:stats':
 
-	The following object(s) are masked from package:stats :
+    IQR, mad, median, var
 
-	 IQR,
-	 mad,
-	 median,
-	 var 
-
 Loading required package: distrMod
 Loading required package: RandVar
-:RandVar>  Implementation of random variables (version 0.7)
+:RandVar>  Implementation of random variables (version 0.8)
 :RandVar> 
 :RandVar>  For more information see ?"RandVar", NEWS("RandVar"), as
 :RandVar>  well as
@@ -198,8 +124,8 @@
 
 Loading required package: MASS
 Loading required package: stats4
-:distrMod>  Object orientated implementation of probability models
-:distrMod>  (version 2.2)
+:distrMod>  Object oriented implementation of probability models
+:distrMod>  (version 2.3)
 :distrMod> 
 :distrMod>  Some functions from pkg's 'base' and 'stats' are
 :distrMod>  intentionally masked ---see distrModMASK().
@@ -210,25 +136,30 @@
 :distrMod>  For more information see ?"distrMod",
 :distrMod>  NEWS("distrMod"), as well as
 :distrMod>    http://distr.r-forge.r-project.org/
-:distrMod>  Package "distrDoc" provides a vignette to this package
+:distrMod>  There is a vignette to this package; try
+:distrMod>  vignette("distrMod").
+:distrMod>  Package "distrDoc" provides a vignette to the other
+:distrMod>  distrXXX packages,
 :distrMod>  as well as to several related packages; try
 :distrMod>  vignette("distr").
 
 
 Attaching package: 'distrMod'
 
+The following object(s) are masked from 'package:stats4':
 
-	The following object(s) are masked from package:stats4 :
+    confint
 
-	 confint 
+The following object(s) are masked from 'package:stats':
 
+    confint
 
-	The following object(s) are masked from package:stats :
+The following object(s) are masked from 'package:base':
 
-	 confint 
+    norm
 
 Loading required package: RobAStBase
-:RobAStBase>  Robust Asymptotic Statistics (version 0.7)
+:RobAStBase>  Robust Asymptotic Statistics (version 0.8)
 :RobAStBase> 
 :RobAStBase>  Some functions from pkg's 'stats' and 'graphics'
 :RobAStBase>  are intentionally masked ---see RobAStBaseMASK().
@@ -243,20 +174,18 @@
 
 Attaching package: 'RobAStBase'
 
+The following object(s) are masked from 'package:stats':
 
-	The following object(s) are masked from package:stats :
+    start
 
-	 start 
+The following object(s) are masked from 'package:graphics':
 
+    clip
 
-	The following object(s) are masked from package:graphics :
-
-	 clip 
-
 > 
 > assign(".oldSearch", search(), pos = 'CheckExEnv')
-> assign(".oldNS", loadedNamespaces(), pos = 'CheckExEnv')
-> cleanEx(); nameEx("0ROptEst-package")
+> cleanEx()
+> nameEx("0ROptEst-package")
 > ### * 0ROptEst-package
 > 
 > flush(stderr()); flush(stdout())
@@ -307,22 +236,22 @@
 > robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
 > estimate(robest)
   lambda 
-3.908322 
+3.908135 
 > ## check influence curve
 > checkIC(pIC(robest))
-precision of centering:	 2.6415e-16 
+precision of centering:	 -2.707017e-08 
 precision of Fisher consistency:
               lambda
-lambda -1.968972e-06
+lambda -1.980378e-06
 maximum deviation 
-     1.968972e-06 
+     1.980378e-06 
 > ## plot influence curve
 > plot(pIC(robest))
 > ## confidence interval based on LAN - neglecting bias
 > confint(robest)
 A[n] asymptotic (LAN-based) confidence interval:
           2.5 %   97.5 %
-lambda 3.826171 3.990474
+lambda 3.826169 3.990102
 Type of estimator: 3-step estimate
 samplesize:   2608
 Call by which estimate was produced:
@@ -332,8 +261,8 @@
 A[n] asymptotic (LAN-based), uniform (bias-aware)
  confidence interval:
 for symmetric Bias
-          2.5 %  97.5 %
-lambda 3.759634 4.05701
+          2.5 %   97.5 %
+lambda 3.761616 4.054655
 Type of estimator: 3-step estimate
 samplesize:   2608
 Call by which estimate was produced:
@@ -341,7 +270,149 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("cniperCont")
+> cleanEx()
+> nameEx("asAnscombe-class")
+> ### * asAnscombe-class
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: asAnscombe-class
+> ### Title: Asymptotic Anscombe risk
+> ### Aliases: asAnscombe-class eff eff,asAnscombe-method
+> ###   show,asAnscombe-method
+> ### Keywords: classes
+> 
+> ### ** Examples
+> 
+> new("asAnscombe")
+An object of class “asAnscombe” 
+risk type:	 optimal bias robust IC for given ARE in the ideal model 
+ARE in the ideal model:	 0.95 
+> 
+> 
+> 
+> cleanEx()
+> nameEx("asAnscombe")
+> ### * asAnscombe
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: asAnscombe
+> ### Title: Generating function for asAnscombe-class
+> ### Aliases: asAnscombe
+> ### Keywords: robust
+> 
+> ### ** Examples
+> 
+> asAnscombe()
+An object of class “asAnscombe” 
+risk type:	 optimal bias robust IC for given ARE in the ideal model 
+ARE in the ideal model:	 0.95 
+> 
+> ## The function is currently defined as
+> function(eff = .95, biastype = symmetricBias(), normtype = NormType()){ 
++     new("asAnscombe", eff = eff, biastype = biastype, normtype = normtype) }
+function (eff = 0.95, biastype = symmetricBias(), normtype = NormType()) 
+{
+    new("asAnscombe", eff = eff, biastype = biastype, normtype = normtype)
+}
+> 
+> 
+> 
+> cleanEx()
+> nameEx("asL1-class")
+> ### * asL1-class
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: asL1-class
+> ### Title: Asymptotic mean absolute error
+> ### Aliases: asL1-class
+> ### Keywords: classes
+> 
+> ### ** Examples
+> 
+> new("asMSE")
+An object of class “asMSE” 
+risk type:	 asymptotic mean square error 
+> 
+> 
+> 
+> cleanEx()
+> nameEx("asL1")
+> ### * asL1
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: asL1
+> ### Title: Generating function for asMSE-class
+> ### Aliases: asL1
+> ### Keywords: robust
+> 
+> ### ** Examples
+> 
+> asL1()
+An object of class “asL1” 
+risk type:	 asymptotic mean absolute error 
+> 
+> ## The function is currently defined as
+> function(biastype = symmetricBias(), normtype = NormType()){ 
++          new("asL1", biastype = biastype, normtype = normtype) }
+function (biastype = symmetricBias(), normtype = NormType()) 
+{
+    new("asL1", biastype = biastype, normtype = normtype)
+}
+> 
+> 
+> 
+> cleanEx()
+> nameEx("asL4-class")
+> ### * asL4-class
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: asL4-class
+> ### Title: Asymptotic mean power 4 error
+> ### Aliases: asL4-class
+> ### Keywords: classes
+> 
+> ### ** Examples
+> 
+> new("asMSE")
+An object of class “asMSE” 
+risk type:	 asymptotic mean square error 
+> 
+> 
+> 
+> cleanEx()
+> nameEx("asL4")
+> ### * asL4
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: asL4
+> ### Title: Generating function for asL4-class
+> ### Aliases: asL4
+> ### Keywords: robust
+> 
+> ### ** Examples
+> 
+> asL4()
+An object of class “asL4” 
+risk type:	 asymptotic mean power 4 error 
+> 
+> ## The function is currently defined as
+> function(biastype = symmetricBias(), normtype = NormType()){ 
++          new("asL4", biastype = biastype, normtype = normtype) }
+function (biastype = symmetricBias(), normtype = NormType()) 
+{
+    new("asL4", biastype = biastype, normtype = normtype)
+}
+> 
+> 
+> 
+> cleanEx()
+> nameEx("cniperCont")
 > ### * cniperCont
 > 
 > flush(stderr()); flush(stdout())
@@ -386,7 +457,8 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("getL1normL2deriv")
+> cleanEx()
+> nameEx("getL1normL2deriv")
 > ### * getL1normL2deriv
 > 
 > flush(stderr()); flush(stdout())
@@ -404,7 +476,8 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("getL2normL2deriv")
+> cleanEx()
+> nameEx("getL2normL2deriv")
 > ### * getL2normL2deriv
 > 
 > flush(stderr()); flush(stdout())
@@ -420,7 +493,218 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("leastFavorableRadius")
+> cleanEx()
+> nameEx("getMaxIneff")
+> ### * getMaxIneff
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: getMaxIneff
+> ### Title: getMaxIneff - computation of the maximal inefficiency of an IC
+> ### Aliases: getMaxIneff
+> ### Keywords: robust
+> 
+> ### ** Examples
+> 
+> N0 <- NormLocationFamily(mean=2, sd=3)
+> ## L_2 family + infinitesimal neighborhood
+> neighbor <- ContNeighborhood(radius = 0.5)
+> N0.Rob1 <- InfRobModel(center = N0, neighbor = neighbor)
+> ## OBRE solution (ARE 95%)
+> N0.ICA <- optIC(model = N0.Rob1, risk = asAnscombe(.95))
+minimal bound:	 3.759947 
+minimal bound:	 3.759947 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.0009839269 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.045311 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.0389404 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04095049 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04096877 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04096873 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04096877 
+> ## OMSE solution radius 0.5
+> N0.ICM <- optIC(model=N0.Rob1, risk=asMSE())
+> ## RMX solution 
+> N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
+> 
+> getMaxIneff(N0.ICA,neighbor)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 1.658389
+> getMaxIneff(N0.ICM,neighbor)
+[1] 1.265537
+> getMaxIneff(N0.ICR,neighbor)
+[1] 1.180746
+> 
+> N0ls <- NormLocationScaleFamily()
+> ICsc <- makeIC(list(sin,cos),N0ls)
+> getMaxIneff(ICsc,neighbor)
+Warning in A[DA.comp] <- matrix(param[1:lA.comp], ncol = k, nrow = p) :
+  number of items to replace is not a multiple of replacement length
+[1] 2.679436
+> 
+> 
+> 
+> 
+> cleanEx()
+> nameEx("getReq")
+> ### * getReq
+> 
+> flush(stderr()); flush(stdout())
+> 
+> ### Name: getReq
+> ### Title: getReq - computation of the radius interval where IC1 is better
+> ###   than IC2
+> ### Aliases: getReq
+> ### Keywords: robust
+> 
+> ### ** Examples
+> 
+> N0 <- NormLocationFamily(mean=2, sd=3)
+> ## L_2 family + infinitesimal neighborhood
+> neighbor <- ContNeighborhood(radius = 0.5)
+> N0.Rob1 <- InfRobModel(center = N0, neighbor = neighbor)
+> ## OBRE solution (ARE 95%)
+> N0.ICA <- optIC(model = N0.Rob1, risk = asAnscombe(.95))
+minimal bound:	 3.759947 
+minimal bound:	 3.759947 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.0009839269 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.045311 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.0389404 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04095049 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04096877 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04096873 
+minimal bound:	 3.759947 
+maximum iterations reached!
+ achieved precision:	 0.04096877 
+> ## MSE solution
+> N0.ICM <- optIC(model=N0.Rob1, risk=asMSE())
+> ## RMX solution
+> N0.ICR <- radiusMinimaxIC(L2Fam=N0, neighbor=neighbor,risk=asMSE())
+> 
+> getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=1)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.0000000 0.3750825
+> getReq(asMSE(),neighbor,N0.ICA,N0.ICM,n=30)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.00000000 0.06848038
+> getReq(asL1(),neighbor,N0.ICA,N0.ICM,n=30)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.00000000 0.06544434
+> getReq(asL4(),neighbor,N0.ICA,N0.ICM,n=30)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.0000000 0.0754216
+> getReq(asMSE(),neighbor,N0.ICA,N0.ICR,n=30)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.00000000 0.07544307
+> getReq(asL1(),neighbor,N0.ICA,N0.ICR,n=30)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.00000000 0.07161849
+> getReq(asL4(),neighbor,N0.ICA,N0.ICR,n=30)
+Warning in .local(IC, risk, L2Fam, ...) :
+  The maximum deviation from the exact IC properties is 0.0020208733776802
+This is larger than the specified 'tol' => the result may be wrong
+[1] 0.00000000 0.08429762
+> getReq(asMSE(),neighbor,N0.ICM,N0.ICR,n=30)
+[1] 0.0000000 0.1016517
+> 
+> ### when to use MAD and when Qn 
+> ##  for Qn, see C. Croux, P. Rousseeuw (1993). Alternatives to the Median 
+> ##      Absolute Deviation, JASA 88(424):1273-1283
+> L2M <- NormScaleFamily()
+> IC.mad <- makeIC(function(x)sign(abs(x)-qnorm(.75)),L2M)
+$Curve
+An object of class “EuclRandVarList” 
+Domain:	Real Space with dimension 1 
+[[1]]
+length of Map:	 1 
+Range:	Real Space with dimension 1 
+
+$CallL2Fam
+L2Fam at fam.call
+
+An object of class “IC” 
+### name:	 square integrable (partial) influence curve 
+### L2-differentiable parametric family:	 normal scale family 
+
+### 'Curve':	An object of class “EuclRandVarList” 
+Domain:	Real Space with dimension 1 
+[[1]]
+length of Map:	 1 
+Range:	Real Space with dimension 1 
+
+### Infos:
+     method message
+> d.qn <- (2^.5*qnorm(5/8))^-1
+> IC.qn <- makeIC(function(x) d.qn*(1/4 - pnorm(x+1/d.qn) + pnorm(x-1/d.qn)), L2M)
+$Curve
+An object of class “EuclRandVarList” 
+Domain:	Real Space with dimension 1 
+[[1]]
+length of Map:	 1 
+Range:	Real Space with dimension 1 
+
+$CallL2Fam
+L2Fam at fam.call
+
+An object of class “IC” 
+### name:	 square integrable (partial) influence curve 
+### L2-differentiable parametric family:	 normal scale family 
+
+### 'Curve':	An object of class “EuclRandVarList” 
+Domain:	Real Space with dimension 1 
+[[1]]
+length of Map:	 1 
+Range:	Real Space with dimension 1 
+
+### Infos:
+     method message
+> getReq(asMSE(), neighbor, IC.mad, IC.qn)
+[1] 0.5074459       Inf
+> # => MAD is better once r > 0.5144 (i.e. for more than 2 outliers for n = 30)
+> 
+> 
+> 
+> cleanEx()
+> nameEx("leastFavorableRadius")
 > ### * leastFavorableRadius
 > 
 > flush(stderr()); flush(stdout())
@@ -460,7 +744,8 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("lowerCaseRadius")
+> cleanEx()
+> nameEx("lowerCaseRadius")
 > ### * lowerCaseRadius
 > 
 > flush(stderr()); flush(stdout())
@@ -486,7 +771,8 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("optIC")
+> cleanEx()
+> nameEx("optIC")
 > ### * optIC
 > 
 > flush(stderr()); flush(stdout())
@@ -515,7 +801,8 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("optRisk")
+> cleanEx()
+> nameEx("optRisk")
 > ### * optRisk
 > 
 > flush(stderr()); flush(stdout())
@@ -538,7 +825,8 @@
 > 
 > 
 > 
-> cleanEx(); nameEx("radiusMinimaxIC")
+> cleanEx()
+> nameEx("radiusMinimaxIC")
 > ### * radiusMinimaxIC
 > 
 > flush(stderr()); flush(stdout())
@@ -557,14 +845,15 @@
 > checkIC(radIC)
 precision of centering:	 -8.135927e-16 
 precision of Fisher consistency:
-             mean
-mean 2.326918e-06
+              mean
+mean -3.502745e-06
 maximum deviation 
-     2.326918e-06 
+     3.502745e-06 
 > 
 > 
 > 
-> cleanEx(); nameEx("roptest")
+> cleanEx()
+> nameEx("roptest")
 > ### * roptest
 > 
 > flush(stderr()); flush(stdout())
@@ -603,13 +892,13 @@
 > robest1 <- roptest(x, BinomFamily(size = 25), eps = 0.05, steps = 3)
 > estimate(robest1)
      prob 
-0.2564328 
+0.2564327 
 > confint(robest1, method = symmetricBias())
 A[n] asymptotic (LAN-based), uniform (bias-aware)
  confidence interval:
 for symmetric Bias
-         2.5 %    97.5 %
-prob 0.2376412 0.2752244
+        2.5 %    97.5 %
+prob 0.237641 0.2752245
 Type of estimator: 3-step estimate
 samplesize:   100
 Call by which estimate was produced:
@@ -621,7 +910,7 @@
 > confint(robest1)
 A[n] asymptotic (LAN-based) confidence interval:
          2.5 %    97.5 %
-prob 0.2382146 0.2746511
+prob 0.2382143 0.2746511
 Type of estimator: 3-step estimate
 samplesize:   100
 Call by which estimate was produced:
@@ -630,56 +919,24 @@
 size 
   25 
 > plot(pIC(robest1))
-> qqplot(x, robest1, cex.pch=1.5, exp.cex2.pch = -.25,
-+        exp.fadcol.pch = .55, jit.fac=.9)
-$x
-  [1]  2.000000  2.859170  2.945003  3.072887  3.127441  3.867602  3.879563
-  [8]  3.969417  3.974350  3.991833  3.999465  4.003945  4.006687  4.022602
- [15]  4.037292  4.114708  4.136915  4.173363  4.879410  4.885243  4.898712
- [22]  4.901497  4.909182  4.912744  4.930320  4.954232  5.002498  5.006047
- [29]  5.028207  5.091694  5.094311  5.094760  5.830003  5.859251  5.866496
- [36]  5.869906  5.871337  5.875739  5.914491  5.942393  5.957418  5.960428
- [43]  5.989444  5.994596  6.003645  6.008635  6.019764  6.053849  6.068266
- [50]  6.103515  6.152629  6.155848  6.159465  6.161268  6.836726  6.837265
- [57]  6.843205  6.867310  6.867319  6.885221  6.912581  6.916303  6.927515
- [64]  6.930985  6.935737  6.970428  6.974665  6.982979  6.985635  7.043348
- [71]  7.047311  7.078457  7.163208  7.856051  7.881048  7.889195  7.895933
- [78]  7.934172  7.935981  7.937725  7.954303  8.065319  8.147733  8.842397
- [85]  8.867768  8.880952  8.899670  8.912292  8.920068  8.954815  9.009832
- [92]  9.097465  9.156489 10.000000 11.068189 11.166108 11.916822 12.163344
- [99] 20.000000 24.000000
-
-$y
-  [1]  0.8557009  1.8381583  1.8628049  2.8541488  2.9861587  2.9973346
-  [7]  3.0625364  3.1545314  3.8446408  3.8708120  3.8834863  3.8895916
- [13]  3.9161963  3.9550780  3.9641619  4.0791690  4.1128367  4.1228866
- [19]  4.1767957  4.8499210  4.9164361  4.9324963  4.9473983  4.9660483
- [25]  4.9982006  5.0448971  5.0592626  5.0943746  5.1015856  5.1124298
- [31]  5.1278296  5.1632774  5.1689152  5.1750722  5.1785879  5.8477746
- [37]  5.8764171  5.8818480  5.8835227  5.9169458  5.9256980  5.9509309
- [43]  5.9529082  5.9583494  5.9637664  5.9793333  5.9889303  6.0151670
- [49]  6.0295939  6.0634191  6.1123673  6.1692584  6.1762199  6.8429689
- [55]  6.8496061  6.8592763  6.9196659  6.9218382  6.9399801  6.9705958
- [61]  6.9898951  6.9950137  7.0313327  7.0811459  7.1024366  7.1212236
- [67]  7.1214700  7.1252376  7.1391252  7.1731665  7.8456154  7.8837900
- [73]  7.8998530  7.9029679  7.9400675  7.9712777  7.9772952  7.9870265
- [79]  8.0223836  8.0715768  8.0730003  8.1313307  8.1542540  8.9035780
- [85]  8.9066922  8.9746968  8.9975947  9.0231350  9.0562184  9.1068610
- [91]  9.1194018  9.1722795  9.8608628  9.8717057  9.8730362 10.1667923
- [97] 10.8757464 11.0025328 11.1530828 11.9453887
-
+> qq1 <- qqplot(x, robest1, cex.pch=1.5, exp.cex2.pch = -.25,
++               exp.fadcol.pch = .55, jit.fac=.9)
+> str(qq1)
+List of 2
+ $ x: num [1:100] 2 2.86 2.95 3.07 3.13 ...
+ $ y: num [1:100] 0.856 1.838 1.863 2.854 2.986 ...
 > 
 > ## compute optimally robust estimator (unknown contamination)
 > robest2 <- roptest(x, BinomFamily(size = 25), eps.lower = 0, eps.upper = 0.2, steps = 3)
 > estimate(robest2)
      prob 
-0.2563208 
+0.2564060 
 > confint(robest2, method = symmetricBias())
 A[n] asymptotic (LAN-based), uniform (bias-aware)
  confidence interval:
 for symmetric Bias
          2.5 %    97.5 %
-prob 0.2373697 0.2752720
+prob 0.2375772 0.2752347
 Type of estimator: 3-step estimate
 samplesize:   100
 Call by which estimate was produced:
@@ -695,13 +952,13 @@
 +                    neighbor = TotalVarNeighborhood(), steps = 3)
 > estimate(robest3)
      prob 
-0.2563266 
+0.2563265 
 > confint(robest3, method = symmetricBias())
 A[n] asymptotic (LAN-based), uniform (bias-aware)
  confidence interval:
 for symmetric Bias
          2.5 %    97.5 %
-prob 0.2317268 0.2809265
+prob 0.2375738 0.2750792
 Type of estimator: 3-step estimate
 samplesize:   100
 Call by which estimate was produced:
@@ -717,13 +974,13 @@
 +                    neighbor = TotalVarNeighborhood(), steps = 3)
 > estimate(robest4)
      prob 
-0.2561783 
+0.2563281 
 > confint(robest4, method = symmetricBias())
 A[n] asymptotic (LAN-based), uniform (bias-aware)
  confidence interval:
 for symmetric Bias
          2.5 %    97.5 %
-prob 0.2303988 0.2819579
+prob 0.2375777 0.2750785
 Type of estimator: 3-step estimate
 samplesize:   100
 Call by which estimate was produced:
@@ -760,1081 +1017,37 @@
 > robest <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
 > estimate(robest)
   lambda 
-3.908322 
+3.908135 
 > confint(robest, symmetricBias())
 A[n] asymptotic (LAN-based), uniform (bias-aware)
  confidence interval:
 for symmetric Bias
-          2.5 %  97.5 %
-lambda 3.759634 4.05701
+          2.5 %   97.5 %
+lambda 3.761616 4.054655
 Type of estimator: 3-step estimate
 samplesize:   2608
 Call by which estimate was produced:
 roptest(x = x, L2Fam = PoisFamily(), eps.upper = 0.1, steps = 3)
 > plot(pIC(robest))
-> qqplot(x, robest, cex.pch=1.5, exp.cex2.pch = -.25,
-+        exp.fadcol.pch = .55, jit.fac=.9)
-$x
-   [1] -0.1793041795 -0.1759861769 -0.1650497656 -0.1612263552 -0.1604914454
-   [6] -0.1566180292 -0.1547882355 -0.1539141720 -0.1492880379 -0.1421819501
-  [11] -0.1354595166 -0.1284600289 -0.1216407305 -0.1187123112 -0.0860652946
-  [16] -0.0848703421 -0.0741629524 -0.0691011184 -0.0559845383 -0.0513339317
-  [21] -0.0480370981 -0.0467019431 -0.0436466014 -0.0339251104 -0.0276845006
-  [26] -0.0235016493 -0.0210747085 -0.0206963610 -0.0088567486 -0.0087029861
-  [31] -0.0064189536 -0.0019243706  0.0003061754  0.0053037550  0.0136313722
-  [36]  0.0281382900  0.0373652645  0.0395096166  0.0568181049  0.0586839484
-  [41]  0.0702044087  0.0741092423  0.0862710285  0.0891486229  0.1054472486
-  [46]  0.1086075740  0.1162055900  0.1218182640  0.1365959866  0.1513864226
-  [51]  0.1557258942  0.1568564884  0.1585111364  0.1593019161  0.1671541337
-  [56]  0.1753566350  0.1777497214  0.8206612690  0.8229262439  0.8239729856
-  [61]  0.8281868407  0.8305757796  0.8310723656  0.8326999961  0.8355585732
-  [66]  0.8378752902  0.8398591431  0.8406541642  0.8409931813  0.8433641932
-  [71]  0.8476829337  0.8538565200  0.8539804964  0.8542547681  0.8552110735
-  [76]  0.8583933951  0.8615232369  0.8621911180  0.8623794204  0.8661980350
-  [81]  0.8678180127  0.8682800816  0.8691355310  0.8730931763  0.8750037309
-  [86]  0.8759444402  0.8770225444  0.8787107722  0.8791575668  0.8825149192
-  [91]  0.8874734936  0.8883207704  0.8897622658  0.8909728169  0.8917991858
-  [96]  0.8927138863  0.8965523245  0.9008669832  0.9064418583  0.9069685200
- [101]  0.9074643792  0.9107235271  0.9121536095  0.9199569112  0.9210700769
- [106]  0.9254615402  0.9286095774  0.9331126588  0.9337162284  0.9369514506
- [111]  0.9383477380  0.9407154735  0.9411203288  0.9412761912  0.9424837941
- [116]  0.9430515644  0.9446183559  0.9453607844  0.9454781263  0.9462252053
- [121]  0.9483909064  0.9502819066  0.9533115229  0.9535180736  0.9538464654
- [126]  0.9565396211  0.9567541329  0.9577016644  0.9597082560  0.9612517187
- [131]  0.9618216605  0.9629732560  0.9632237059  0.9636769201  0.9647909426
- [136]  0.9660845800  0.9669799935  0.9695144805  0.9706020370  0.9762511055
- [141]  0.9769515046  0.9821747574  0.9826629447  0.9854471484  0.9859222646
- [146]  0.9881614416  0.9900043129  0.9914276866  0.9927408860  0.9948419585
- [151]  0.9982230780  0.9992324987  1.0008102935  1.0061235375  1.0091099813
- [156]  1.0100975965  1.0117364390  1.0119018181  1.0139659478  1.0142063300
- [161]  1.0151125407  1.0192512533  1.0195037420  1.0223157134  1.0243674631
- [166]  1.0259812935  1.0316540557  1.0317039015  1.0318309085  1.0347214441
- [171]  1.0383187581  1.0435874658  1.0438383806  1.0444368554  1.0455957451
- [176]  1.0485552876  1.0496471404  1.0518786023  1.0525842197  1.0528045084
- [181]  1.0534783411  1.0546987396  1.0552057995  1.0557038353  1.0569007193
- [186]  1.0588033811  1.0603891459  1.0604982174  1.0633073538  1.0666387019
- [191]  1.0677790855  1.0682046519  1.0684924203  1.0690939620  1.0706309198
- [196]  1.0751464184  1.0752310714  1.0765144441  1.0772984619  1.0776999821
- [201]  1.0779722124  1.0819961217  1.0832937684  1.0837784864  1.0854299685
- [206]  1.0899357994  1.0905415005  1.0923921667  1.0931308505  1.0937127814
- [211]  1.0974353074  1.1000953104  1.1052563522  1.1075732378  1.1095806777
- [216]  1.1131306302  1.1156184345  1.1179783971  1.1182723695  1.1185123830
- [221]  1.1185567496  1.1188884534  1.1233323652  1.1235232534  1.1246484710
- [226]  1.1258045535  1.1258387574  1.1266003503  1.1287005533  1.1303322548
- [231]  1.1309070039  1.1322059634  1.1334899991  1.1346938779  1.1359281024
- [236]  1.1456367932  1.1462734941  1.1466249098  1.1489624711  1.1541439174
- [241]  1.1543598013  1.1558111337  1.1565136419  1.1577436900  1.1578289484
- [246]  1.1580892142  1.1591469674  1.1608544149  1.1616177439  1.1623423633
- [251]  1.1638528258  1.1640432767  1.1646099600  1.1647491475  1.1664399911
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/robast -r 444


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