[Distr-commits] r1197 - pkg/distrMod/tests/Examples

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Wed Jul 11 19:44:45 CEST 2018


Author: stamats
Date: 2018-07-11 19:44:44 +0200 (Wed, 11 Jul 2018)
New Revision: 1197

Modified:
   pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save
Log:
Update

Modified: pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save
===================================================================
--- pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save	2018-07-08 16:57:09 UTC (rev 1196)
+++ pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save	2018-07-11 17:44:44 UTC (rev 1197)
@@ -1,3179 +1,3095 @@
-
-R Under development (unstable) (2015-05-02 r68310) -- "Unsuffered Consequences"
-Copyright (C) 2015 The R Foundation for Statistical Computing
-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.
-Type 'license()' or 'licence()' for distribution details.
-
-  Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-> pkgname <- "distrMod"
-> source(file.path(R.home("share"), "R", "examples-header.R"))
-> options(warn = 1)
-> library('distrMod')
-Loading required package: distr
-Loading required package: startupmsg
-:startupmsg>  Utilities for Start-Up Messages (version 0.9.1)
-:startupmsg> 
-:startupmsg>  For more information see ?"startupmsg",
-:startupmsg>  NEWS("startupmsg")
-
-Loading required package: sfsmisc
-Loading required package: SweaveListingUtils
-:SweaveListingUtils>  Utilities for Sweave Together with
-:SweaveListingUtils>  TeX 'listings' Package (version
-:SweaveListingUtils>  0.7)
-: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().
-:SweaveListingUtils> 
-:SweaveListingUtils>  Note that global options are
-:SweaveListingUtils>  controlled by
-:SweaveListingUtils>  SweaveListingoptions() ---c.f.
-:SweaveListingUtils>  ?"SweaveListingoptions".
-:SweaveListingUtils> 
-:SweaveListingUtils>  For more information see
-:SweaveListingUtils>  ?"SweaveListingUtils",
-:SweaveListingUtils>  NEWS("SweaveListingUtils")
-:SweaveListingUtils>  There is a vignette to this
-:SweaveListingUtils>  package; try
-:SweaveListingUtils>  vignette("ExampleSweaveListingUtils").
-
-
-Attaching package: ‘SweaveListingUtils’
-
-The following objects are masked from ‘package:base’:
-
-    library, require
-
-:distr>  Object Oriented Implementation of Distributions (version
-:distr>  2.6)
-:distr> 
-:distr>  Attention: Arithmetics on distribution objects are
-:distr>  understood as operations on corresponding random variables
-:distr>  (r.v.s); see distrARITH().
-:distr> 
-:distr>  Some functions from package 'stats' are intentionally masked
-:distr>  ---see distrMASK().
-:distr> 
-:distr>  Note that global options are controlled by distroptions()
-:distr>  ---c.f. ?"distroptions".
-:distr> 
-:distr>  For more information see ?"distr", NEWS("distr"), as well as
-:distr>    http://distr.r-forge.r-project.org/
-:distr>  Package "distrDoc" provides a vignette to this package as
-:distr>  well as to several extension packages; try
-:distr>  vignette("distr").
-
-
-Attaching package: ‘distr’
-
-The following objects are masked from ‘package:stats’:
-
-    df, qqplot, sd
-
-Loading required package: distrEx
-:distrEx>  Extensions of Package 'distr' (version 2.6)
-:distrEx> 
-:distrEx>  Note: Packages "e1071", "moments", "fBasics" should be
-:distrEx>  attached /before/ package "distrEx". See
-:distrEx>  distrExMASK().Note: Extreme value distribution
-:distrEx>  functionality has been moved to
-:distrEx> 
-:distrEx>        package "RobExtremes". See distrExMOVED().
-:distrEx> 
-:distrEx>  For more information see ?"distrEx", NEWS("distrEx"), as
-:distrEx>  well as
-:distrEx>    http://distr.r-forge.r-project.org/
-:distrEx>  Package "distrDoc" provides a vignette to this package
-:distrEx>  as well as to several related packages; try
-:distrEx>  vignette("distr").
-
-
-Attaching package: ‘distrEx’
-
-The following objects are masked from ‘package:stats’:
-
-    IQR, mad, median, var
-
-Loading required package: RandVar
-:RandVar>  Implementation of random variables (version 0.9.2)
-:RandVar> 
-:RandVar>  For more information see ?"RandVar", NEWS("RandVar"), as
-:RandVar>  well as
-:RandVar>    http://robast.r-forge.r-project.org/
-:RandVar>  This package also includes a vignette; try
-:RandVar>  vignette("RandVar").
-
-Loading required package: MASS
-Loading required package: stats4
-:distrMod>  Object Oriented Implementation of Probability Models
-:distrMod>  (version 2.6)
-:distrMod> 
-:distrMod>  Some functions from pkg's 'base' and 'stats' are
-:distrMod>  intentionally masked ---see distrModMASK().
-:distrMod> 
-:distrMod>  Note that global options are controlled by
-:distrMod>  distrModoptions() ---c.f. ?"distrModoptions".
-:distrMod> 
-:distrMod>  For more information see ?"distrMod",
-:distrMod>  NEWS("distrMod"), as well as
-:distrMod>    http://distr.r-forge.r-project.org/
-: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 is masked from ‘package:stats4’:
-
-    confint
-
-The following object is masked from ‘package:stats’:
-
-    confint
-
-The following object is masked from ‘package:base’:
-
-    norm
-
-> 
-> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
-> cleanEx()
-> nameEx("BetaFamily")
-> ### * BetaFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: BetaFamily
-> ### Title: Generating function for Beta families
-> ### Aliases: BetaFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> (B1 <- BetaFamily())
-An object of class "BetaFamily"
-### name:	Beta family
-
-### distribution:	Distribution Object of Class: Beta
- shape1: 1
- shape2: 1
- ncp: 0
-
-### param:	An object of class "ParamFamParameter"
-name:	shape1 and shape2
-shape1:	1
-shape2:	1
-trafo:
-       shape1 shape2
-shape1      1      0
-shape2      0      1
-
-### props:
-[1] "The Beta family is invariant in the following sense"
-[2] "if (x_i)~Beta(s1,s2) then (1-x_i)~Beta(s2,s1)"      
-> FisherInfo(B1)
-An object of class "PosSemDefSymmMatrix"
-           shape1     shape2
-shape1  1.0000000 -0.6449341
-shape2 -0.6449341  1.0000000
-> checkL2deriv(B1)
-precision of centering:	 3.96327e-05 3.963591e-05 
-precision of Fisher information:
-              shape1        shape2
-shape1 -1.851068e-05  1.648326e-06
-shape2  1.648326e-06 -1.851068e-05
-precision of Fisher information - relativ error [%]:
-              shape1        shape2
-shape1 -0.0018510679 -0.0002555806
-shape2 -0.0002555806 -0.0018510679
-condition of Fisher information:
-[1] 5.277691
-$maximum.deviation
-[1] 3.963591e-05
-
-> 
-> 
-> 
-> cleanEx()
-> nameEx("BiasType-class")
-> ### * BiasType-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: BiasType-class
-> ### Title: Bias Type
-> ### Aliases: BiasType-class name,BiasType-method name<-,BiasType-method
-> ### Keywords: classes
-> 
-> ### ** Examples
-> 
-> aB <- positiveBias()
-> name(aB)
-[1] "positive Bias"
-> 
-> 
-> 
-> cleanEx()
-> nameEx("BinomFamily")
-> ### * BinomFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: BinomFamily
-> ### Title: Generating function for Binomial families
-> ### Aliases: BinomFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> (B1 <- BinomFamily(size = 25, prob = 0.25))
-An object of class "BinomFamily"
-### name:	Binomial family
-
-### distribution:	Distribution Object of Class: Binom
- size: 25
- prob: 0.25
-
-### param:	An object of class "ParamFamParameter"
-name:	probability of success
-prob:	0.25
-fixed part of param.:
-	size:	25
-trafo:
-     prob
-prob    1
-
-### props:
-[1] "The Binomial family is symmetric with respect to prob = 0.5;"
-[2] "i.e., d(Binom(size, prob))(k)=d(Binom(size,1-prob))(size-k)" 
-> plot(B1)
-> FisherInfo(B1)
-An object of class "PosSemDefSymmMatrix"
-         prob
-prob 133.3333
-> checkL2deriv(B1)
-precision of centering:	 -1.099042e-15 
-precision of Fisher information:
-             prob
-prob 2.842171e-14
-precision of Fisher information - relativ error [%]:
-             prob
-prob 2.131628e-14
-condition of Fisher information:
-[1] 1
-$maximum.deviation
-[1] 2.842171e-14
-
-> 
-> 
-> 
-> cleanEx()
-> nameEx("CauchyLocationScaleFamily")
-> ### * CauchyLocationScaleFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: CauchyLocationScaleFamily
-> ### Title: Generating function for Cauchy location and scale families
-> ### Aliases: CauchyLocationScaleFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> (C1 <- CauchyLocationScaleFamily())
-An object of class "CauchyLocationScaleFamily"
-### name:	Cauchy Location and scale family
-
-### distribution:	Distribution Object of Class: Cauchy
- location: 0
- scale: 1
-
-### param:	An object of class "ParamWithScaleFamParameter"
-name:	location and scale
-loc:	0
-scale:	1
-trafo:
-      loc scale
-loc     1     0
-scale   0     1
-
-### props:
-[1] "The Cauchy Location and scale family is invariant under"  
-[2] "the group of transformations 'g(x) = scale*x + loc'"      
-[3] "with location parameter 'loc' and scale parameter 'scale'"
-> plot(C1)
-> FisherInfo(C1)
-An object of class "PosDefSymmMatrix"
-      loc scale
-loc   0.5   0.0
-scale 0.0   0.5
-> ### need smaller integration range:
-> distrExoptions("ElowerTruncQuantile"=1e-4,"EupperTruncQuantile"=1e-4)
-> checkL2deriv(C1)
-precision of centering:	 0 -0.02119711 
-precision of Fisher information:
-                loc       scale
-loc   -3.137524e-05  0.00000000
-scale  0.000000e+00 -0.02118143
-precision of Fisher information - relativ error [%]:
-               loc     scale
-loc   -0.006275047       NaN
-scale          NaN -4.236286
-condition of Fisher information:
-[1] 1
-$maximum.deviation
-[1] 0.02119711
-
-> distrExoptions("ElowerTruncQuantile"=1e-7,"EupperTruncQuantile"=1e-7)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("Confint-class")
-> ### * Confint-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: Confint-class
-> ### Title: Confint-class
-> ### Aliases: Confint-class type,Confint-method call.estimate
-> ###   call.estimate,Confint-method confint,Confint,missing-method
-> ###   name.estimate name.estimate,Confint-method trafo.estimate
-> ###   trafo.estimate,Confint-method samplesize.estimate
-> ###   samplesize.estimate,Confint-method completecases.estimate
-> ###   completecases.estimate,Confint-method nuisance.estimate
-> ###   nuisance.estimate,Confint-method fixed.estimate
-> ###   fixed.estimate,Confint-method show,Confint-method
-> ###   print,Confint-method
-> ### Keywords: classes
-> 
-> ### ** Examples
-> 
-> ## some transformation
-> mtrafo <- function(x){
-+      nms0 <- c("scale","shape")
-+      nms <- c("shape","rate")
-+      fval0 <- c(x[2], 1/x[1])
-+      names(fval0) <- nms
-+      mat0 <- matrix( c(0, -1/x[1]^2, 1, 0), nrow = 2, ncol = 2,
-+                      dimnames = list(nms,nms0))                          
-+      list(fval = fval0, mat = mat0)}
-> 
-> x <- rgamma(50, scale = 0.5, shape = 3)
-> 
-> ## parametric family of probability measures
-> G <- GammaFamily(scale = 1, shape = 2, trafo = mtrafo)
-> ## MLE
-> res <- MLEstimator(x = x, ParamFamily = G)
-> ci <- confint(res)
-> print(ci, digits = 4, show.details="maximal")
-A[n] asymptotic (CLT-based) confidence interval:
-      2.5 % 97.5 %
-shape 2.530  5.591
-rate  1.751  4.097
-Type of estimator: Maximum likelihood estimate
-samplesize:   50
-Call by which estimate was produced:
-MLEstimator(x = x, ParamFamily = G)
-Transformation of main parameter by which estimate was produced:
-function (x) 
-{
-    nms0 <- c("scale", "shape")
-    nms <- c("shape", "rate")
-    fval0 <- c(x[2], 1/x[1])
-    names(fval0) <- nms
-    mat0 <- matrix(c(0, -1/x[1]^2, 1, 0), nrow = 2, ncol = 2, 
-        dimnames = list(nms, nms0))
-    list(fval = fval0, mat = mat0)
-}
-Trafo / derivative matrix at which estimate was produced:
-       scale shape
-shape  0.000     1
-rate  -8.549     0
-> print(ci, digits = 4, show.details="medium")
-A[n] asymptotic (CLT-based) confidence interval:
-      2.5 % 97.5 %
-shape 2.530  5.591
-rate  1.751  4.097
-Type of estimator: Maximum likelihood estimate
-samplesize:   50
-Call by which estimate was produced:
-MLEstimator(x = x, ParamFamily = G)
-> print(ci, digits = 4, show.details="minimal")
-A[n] asymptotic (CLT-based) confidence interval:
-      2.5 % 97.5 %
-shape 2.530  5.591
-rate  1.751  4.097
-> 
-> 
-> 
-> cleanEx()
-> nameEx("Estimate-class")
-> ### * Estimate-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: Estimate-class
-> ### Title: Estimate-class.
-> ### Aliases: Estimate-class name,Estimate-method name<-,Estimate-method
-> ###   estimate estimate,Estimate-method estimate.call
-> ###   estimate.call,Estimate-method Infos Infos,Estimate-method samplesize
-> ###   samplesize,Estimate-method completecases
-> ###   completecases,Estimate-method asvar asvar,Estimate-method
-> ###   fixed,Estimate-method asvar<- asvar<-,Estimate-method
-> ###   nuisance,Estimate-method main,Estimate-method Infos<-
-> ###   Infos<-,Estimate-method addInfo<- addInfo<-,Estimate-method
-> ###   show,Estimate-method print,Estimate-method untransformed.estimate
-> ###   untransformed.estimate,Estimate-method untransformed.asvar
-> ###   untransformed.asvar,Estimate-method
-> ### Keywords: classes
-> 
-> ### ** Examples
-> 
-> x <- rnorm(100)
-> Estimator(x, estimator = mean, name = "mean")
-Evaluations of mean:
---------------------
-An object of class “Estimate” 
-generated by call
-  Estimator(x = x, estimator = mean, name = "mean")
-samplesize:   100
-estimate:
-    mean1 
-0.1088874 
-> 
-> x1 <- x; x1[sample(1:100,10)] <- NA
-> myEst1 <- Estimator(x1, estimator = mean, name = "mean")
-> samplesize(myEst1)
-[1] 90
-> samplesize(myEst1, onlycomplete = FALSE)
-[1] 100
-> 
-> 
-> 
-> cleanEx()
-> nameEx("Estimator")
-> ### * Estimator
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: Estimator
-> ### Title: Function to compute estimates
-> ### Aliases: Estimator
-> ### Keywords: univar
-> 
-> ### ** Examples
-> 
-> x <- rnorm(100)
-> Estimator(x, estimator = mean, name = "mean")
-Evaluations of mean:
---------------------
-An object of class “Estimate” 
-generated by call
-  Estimator(x = x, estimator = mean, name = "mean")
-samplesize:   100
-estimate:
-    mean1 
-0.1088874 
-> 
-> X <- matrix(rnorm(1000), nrow = 10)
-> Estimator(X, estimator = rowMeans, name = "mean")
-Evaluations of mean:
---------------------
-An object of class “Estimate” 
-generated by call
-  Estimator(x = X, estimator = rowMeans, name = "mean")
-samplesize:   100
-estimate:
-  rowMeans1   rowMeans2   rowMeans3   rowMeans4   rowMeans5   rowMeans6 
--0.10612810  0.22309674 -0.01146361 -0.20224815  0.08660978 -0.13837167 
-  rowMeans7   rowMeans8   rowMeans9  rowMeans10 
--0.03214991 -0.02971528 -0.13027892  0.10496336 
-> 
-> 
-> 
-> cleanEx()
-> nameEx("EvenSymmetric-class")
-> ### * EvenSymmetric-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: EvenSymmetric-class
-> ### Title: Class for Even Functions
-> ### Aliases: EvenSymmetric-class
-> ### Keywords: classes
-> 
-> ### ** Examples
-> 
-> new("EvenSymmetric")
-type of symmetry:	even function
-center of symmetry:
-numeric(0)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("EvenSymmetric")
-> ### * EvenSymmetric
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: EvenSymmetric
-> ### Title: Generating function for EvenSymmetric-class
-> ### Aliases: EvenSymmetric
-> ### Keywords: math
-> 
-> ### ** Examples
-> 
-> EvenSymmetric()
-type of symmetry:	even function
-center of symmetry:
-[1] 0
-> 
-> ## The function is currently defined as
-> function(SymmCenter = 0){ 
-+     new("EvenSymmetric", SymmCenter = SymmCenter) 
-+ }
-function (SymmCenter = 0) 
-{
-    new("EvenSymmetric", SymmCenter = SymmCenter)
-}
-> 
-> 
-> 
-> cleanEx()
-> nameEx("ExpScaleFamily")
-> ### * ExpScaleFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: ExpScaleFamily
-> ### Title: Generating function for exponential scale families
-> ### Aliases: ExpScaleFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> (E1 <- ExpScaleFamily())
-An object of class "ExpScaleFamily"
-### name:	Exponential scale family
-
-### distribution:	Distribution Object of Class: Exp
- rate: 1
-Warning in show(x) :
-  arithmetics on distributions are understood as operations on r.v.'s
-see 'distrARITH()'; for switching off this warning see '?distroptions'
-
-### param:	An object of class "ParamWithScaleFamParameter"
-name:	scale
-scale:	1
-trafo:
-      scale
-scale     1
-
-### props:
-[1] "The Exponential scale family is invariant under"
-[2] "the group of transformations 'g(y) = scale*y'"  
-[3] "with scale parameter 'scale'"                   
-> plot(E1)
-> Map(L2deriv(E1)[[1]])
-[[1]]
-function (x) 
-{
-    ((x - 0)/1 * LogDeriv((x - 0)/1) - 1)/1
-}
-<environment: 0x9873698>
-
-> checkL2deriv(E1)
-precision of centering:	 -1.51181e-06 
-precision of Fisher information:
-             scale
-scale -2.61793e-05
-precision of Fisher information - relativ error [%]:
-            scale
-scale -0.00261793
-condition of Fisher information:
-[1] 1
-$maximum.deviation
-[1] 2.61793e-05
-
-> 
-> 
-> 
-> cleanEx()
-> nameEx("FunSymmList-class")
-> ### * FunSymmList-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: FunSymmList-class
-> ### Title: List of Symmetries for a List of Functions
-> ### Aliases: FunSymmList-class
-> ### Keywords: classes
-> 
-> ### ** Examples
-> 
-> new("FunSymmList", list(NonSymmetric(), EvenSymmetric(SymmCenter = 1), 
-+                         OddSymmetric(SymmCenter = 2)))
-An object of class "FunSymmList"
-[[1]]
-type of symmetry:	non-symmetric function
-NULL
-
-[[2]]
-type of symmetry:	even function
-center of symmetry:
-[1] 1
-
-[[3]]
-type of symmetry:	odd function
-center of symmetry:
-[1] 2
-
-> 
-> 
-> 
-> cleanEx()
-> nameEx("FunSymmList")
-> ### * FunSymmList
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: FunSymmList
-> ### Title: Generating function for FunSymmList-class
-> ### Aliases: FunSymmList
-> ### Keywords: math
-> 
-> ### ** Examples
-> 
-> FunSymmList(NonSymmetric(), EvenSymmetric(SymmCenter = 1), 
-+             OddSymmetric(SymmCenter = 2))
-An object of class "FunSymmList"
-[[1]]
-type of symmetry:	non-symmetric function
-NULL
-
-[[2]]
-type of symmetry:	even function
-center of symmetry:
-[1] 1
-
-[[3]]
-type of symmetry:	odd function
-center of symmetry:
-[1] 2
-
-> 
-> ## The function is currently defined as
-> function (...){
-+     new("FunSymmList", list(...))
-+ }
-function (...) 
-{
-    new("FunSymmList", list(...))
-}
-> 
-> 
-> 
-> cleanEx()
-> nameEx("GammaFamily")
-> ### * GammaFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: GammaFamily
-> ### Title: Generating function for Gamma families
-> ### Aliases: GammaFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> (G1 <- GammaFamily())
-An object of class "GammaFamily"
-### name:	Gamma family
-
-### distribution:	Distribution Object of Class: Gammad
- shape: 1
- scale: 1
-
-### param:	An object of class "ParamFamParameter"
-name:	scale and shape
-scale:	1
-shape:	1
-trafo:
-      scale shape
-scale     1     0
-shape     0     1
-Shape parameter must not be negative.
-
-### props:
-[1] "The Gamma family is scale invariant via the parametrization"
-[2] "'(nu,shape)=(log(scale),shape)'"                            
-> FisherInfo(G1)
-An object of class "PosDefSymmMatrix"
-      scale    shape
-scale     1 1.000000
-shape     1 1.644934
-> checkL2deriv(G1)
-precision of centering:	 -1.51181e-06 1.312514e-06 
-precision of Fisher information:
-              scale         shape
-scale -2.617930e-05 -7.165188e-06
-shape -7.165188e-06 -2.862712e-05
-precision of Fisher information - relativ error [%]:
-              scale         shape
-scale -0.0026179301 -0.0007165188
-shape -0.0007165188 -0.0017403202
-condition of Fisher information:
-[1] 10.60328
-$maximum.deviation
-[1] 2.862712e-05
-
-> 
-> 
-> 
-> cleanEx()
-> nameEx("InfoNorm")
-> ### * InfoNorm
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: InfoNorm
-> ### Title: Generating function for InfoNorm-class
-> ### Aliases: InfoNorm
-> ### Keywords: robust
-> 
-> ### ** Examples
-> 
-> InfoNorm()
-An object of class "InfoNorm"
-Slot "QuadForm":
-An object of class "PosSemDefSymmMatrix"
-     [,1]
-[1,]    1
-
-Slot "name":
-[1] "Information matrix Norm"
-
-Slot "fct":
-function (x) 
-QuadFormNorm(x, A = A)
-<bytecode: 0x9731190>
-<environment: 0x9731f38>
-
-> 
-> ## The function is currently defined as
-> function(){ new("InfoNorm") }
-function () 
-{
-    new("InfoNorm")
-}
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2GroupFamily-class")
-> ### * L2GroupFamily-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2GroupParamFamily-class
-> ### Title: L2 differentiable parametric group family
-> ### Aliases: L2GroupParamFamily-class LogDeriv
-> ###   LogDeriv,L2GroupParamFamily-method LogDeriv<-
-> ###   LogDeriv<-,L2GroupParamFamily-method
-> ### Keywords: classes models
-> 
-> ### ** Examples
-> 
-> F1 <- new("L2GroupParamFamily")
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2LocationFamily-class")
-> ### * L2LocationFamily-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2LocationFamily-class
-> ### Title: L2 differentiable parametric group family
-> ### Aliases: L2LocationFamily-class
-> ### Keywords: classes models
-> 
-> ### ** Examples
-> 
-> F1 <- new("L2LocationFamily")
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2LocationFamily")
-> ### * L2LocationFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2LocationFamily
-> ### Title: Generating function for L2LocationFamily-class
-> ### Aliases: L2LocationFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> F1 <- L2LocationFamily()
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2LocationScaleFamily-class")
-> ### * L2LocationScaleFamily-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2LocationScaleFamily-class
-> ### Title: L2 differentiable parametric group family
-> ### Aliases: L2LocationScaleFamily-class
-> ### Keywords: classes models
-> 
-> ### ** Examples
-> 
-> F1 <- new("L2LocationScaleFamily")
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2LocationScaleFamily")
-> ### * L2LocationScaleFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2LocationScaleFamily
-> ### Title: Generating function for L2LocationScaleFamily-class
-> ### Aliases: L2LocationScaleFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> F1 <- L2LocationScaleFamily()
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2LocationUnknownScaleFamily")
-> ### * L2LocationUnknownScaleFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2LocationUnknownScaleFamily
-> ### Title: Generating function for L2LocationScaleFamily-class in nuisance
-> ###   situation
-> ### Aliases: L2LocationUnknownScaleFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> F1 <- L2LocationUnknownScaleFamily()
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2ParamFamily-class")
-> ### * L2ParamFamily-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2ParamFamily-class
-> ### Title: L2 differentiable parametric family
-> ### Aliases: plot plot-methods L2ParamFamily-class FisherInfo
-> ###   FisherInfo,L2ParamFamily,missing-method
-> ###   FisherInfo,L2ParamFamily,ParamFamParameter-method L2deriv
-> ###   L2deriv,L2ParamFamily,missing-method
-> ###   L2deriv,L2ParamFamily,ParamFamParameter-method L2derivSymm
-> ###   L2derivSymm,L2ParamFamily-method L2derivDistr
-> ###   L2derivDistr,L2ParamFamily-method L2derivDistrSymm
-> ###   L2derivDistrSymm,L2ParamFamily-method
-> ###   checkL2deriv,L2ParamFamily-method
-> ###   E,L2ParamFamily,EuclRandVariable,missing-method
-> ###   E,L2ParamFamily,EuclRandMatrix,missing-method
-> ###   E,L2ParamFamily,EuclRandVarList,missing-method
-> ###   plot,L2ParamFamily,missing-method
-> ### Keywords: classes models
-> 
-> ### ** Examples
-> 
-> F1 <- new("L2ParamFamily")
-> plot(F1)
-> 
-> ## selection of subpanels for plotting
-> F2 <- L2LocationScaleFamily()
-> layout(matrix(c(1,2,3,3), nrow=2, byrow=TRUE))
-> plot(F2,mfColRow = FALSE,
-+      to.draw.arg=c("p","q","loc"))
-> plot(F2,mfColRow = FALSE, inner=list("empirical cdf","pseudo-inverse",
-+      "L2-deriv, loc.part"), to.draw.arg=c("p","q","loc"))
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2ParamFamily")
-> ### * L2ParamFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2ParamFamily
-> ### Title: Generating function for L2ParamFamily-class
-> ### Aliases: L2ParamFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> F1 <- L2ParamFamily()
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2ScaleFamily-class")
-> ### * L2ScaleFamily-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2ScaleFamily-class
-> ### Title: L2 differentiable parametric group family
-> ### Aliases: L2ScaleFamily-class
-> ### Keywords: classes models
-> 
-> ### ** Examples
-> 
-> F1 <- new("L2ScaleFamily")
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2ScaleFamily")
-> ### * L2ScaleFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2ScaleFamily
-> ### Title: Generating function for L2ScaleFamily-class
-> ### Aliases: L2ScaleFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> F1 <- L2ScaleFamily()
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("L2ScaleUnknownLocationFamily")
-> ### * L2ScaleUnknownLocationFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: L2ScaleUnknownLocationFamily
-> ### Title: Generating function for L2LocationScaleFamily-class in nuisance
-> ###   situation
-> ### Aliases: L2ScaleUnknownLocationFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> F1 <- L2ScaleUnknownLocationFamily()
-> plot(F1)
-> 
-> 
-> 
-> cleanEx()
-> nameEx("LnormScaleFamily")
-> ### * LnormScaleFamily
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: LnormScaleFamily
-> ### Title: Generating function for lognormal scale families
-> ### Aliases: LnormScaleFamily
-> ### Keywords: models
-> 
-> ### ** Examples
-> 
-> (L1 <- LnormScaleFamily())
-An object of class "LnormScaleFamily"
-### name:	lognormal scale family
-
-### distribution:	Distribution Object of Class: Lnorm
- meanlog: 0
- sdlog: 1
-Warning in show(x) :
-  arithmetics on distributions are understood as operations on r.v.'s
-see 'distrARITH()'; for switching off this warning see '?distroptions'
-
-### param:	An object of class "ParamWithScaleFamParameter"
-name:	scale
-meanlog:	1
-fixed part of param.:
-	:	0
-trafo:
-      scale
-scale     1
-
-### props:
-[1] "The lognormal scale family is invariant under"
-[2] "the group of transformations 'g(y) = scale*y'"
-[3] "with scale parameter 'scale'"                 
-> plot(L1)
-> Map(L2deriv(L1)[[1]])
-[[1]]
-function (x) 
-{
-    ((x - 0)/1 * LogDeriv((x - 0)/1) - 1)/1
-}
-<environment: 0xabd01c0>
-
-> checkL2deriv(L1)
-precision of centering:	 -0.003003394 
-precision of Fisher information:
-            meanlog
-meanlog -0.01027919
-precision of Fisher information - relativ error [%]:
-          meanlog
-meanlog -1.027919
-condition of Fisher information:
-[1] 1
-$maximum.deviation
-[1] 0.01027919
-
-> 
-> 
-> 
-> cleanEx()
-> nameEx("MCEstimate-class")
-> ### * MCEstimate-class
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: MCEstimate-class
-> ### Title: MCEstimate-class.
-> ### Aliases: MCEstimate-class criterion criterion,MCEstimate-method
-> ###   criterion.fct criterion.fct,MCEstimate-method
-> ###   startPar,MCEstimate-method method method,MCEstimate-method optimwarn
-> ###   optimwarn,MCEstimate-method criterion<- criterion<-,MCEstimate-method
-> ###   coerce,MCEstimate,mle-method show,MCEstimate-method
-> ###   profile,MCEstimate-method
-> ### Keywords: classes
-> 
-> ### ** Examples
-> 
-> ## (empirical) Data
-> x <- rgamma(50, scale = 0.5, shape = 3)
-> 
-> ## parametric family of probability measures
-> G <- GammaFamily(scale = 1, shape = 2)
-> 
-> MDEstimator(x, G)
-Evaluations of Minimum Kolmogorov distance estimate:
-----------------------------------------------------
-An object of class “Estimate” 
-generated by call
-  MDEstimator(x = x, ParamFamily = G)
-samplesize:   50
-estimate:
-    scale     shape 
-0.2983286 4.6547001 
-Criterion:
-Kolmogorov distance 
-              1e+20 
-> (m <- MLEstimator(x, G))
-Evaluations of Maximum likelihood estimate:
--------------------------------------------
-An object of class “Estimate” 
-generated by call
-  MLEstimator(x = x, ParamFamily = G)
-samplesize:   50
-estimate:
-     scale        shape   
-  0.34200800   4.06028564 
- (0.07002713) (0.78099026)
-asymptotic (co)variance (multiplied with samplesize):
-           scale     shape
-scale  0.2451899 -2.568863
-shape -2.5688629 30.497289
-Criterion:
-negative log-likelihood 
-                47.9651 
-> m.mle <- as(m,"mle")
-> par(mfrow=c(1,2))
-> profileM <- profile(m)
-> ## plot-profile throws an error
-> 
-> 
-> 
-> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
-> cleanEx()
-> nameEx("MCEstimator")
-> ### * MCEstimator
-> 
-> flush(stderr()); flush(stdout())
-> 
-> ### Name: MCEstimator
-> ### Title: Function to compute minimum criterion estimates
-> ### Aliases: MCEstimator
-> ### Keywords: univar
-> 
-> ### ** Examples
-> 
-> ## (empirical) Data
-> x <- rgamma(50, scale = 0.5, shape = 3)
-> 
-> ## parametric family of probability measures
-> G <- GammaFamily(scale = 1, shape = 2)
-> 
-> ## Maximum Likelihood estimator
-> ## Note: you can directly use function MLEstimator!
-> negLoglikelihood <- function(x, Distribution){
-+     res <- -sum(log(Distribution at d(x)))
-+     names(res) <- "Negative Log-Likelihood"
-+     return(res)
-+ }
-> MCEstimator(x = x, ParamFamily = G, criterion = negLoglikelihood)
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.764,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,5.12 threw an error;
-returning starting par;
-
-Evaluations of Minimum criterion estimate:
-------------------------------------------
-An object of class “Estimate” 
-generated by call
-  MCEstimator(x = x, ParamFamily = G, criterion = negLoglikelihood)
-samplesize:   50
-estimate:
-    scale     shape 
-0.2983286 4.6547001 
-Criterion:
-      
-1e+20 
-> 
-> ## Kolmogorov(-Smirnov) minimum distance estimator
-> ## Note: you can also use function MDEstimator!
-> MCEstimator(x = x, ParamFamily = G, criterion = KolmogorovDist, 
-+             crit.name = "Kolmogorov distance")
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.764,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,5.12 threw an error;
-returning starting par;
-
-Evaluations of Minimum Kolmogorov distance estimate:
-----------------------------------------------------
-An object of class “Estimate” 
-generated by call
-  MCEstimator(x = x, ParamFamily = G, criterion = KolmogorovDist, 
-    crit.name = "Kolmogorov distance")
-samplesize:   50
-estimate:
-    scale     shape 
-0.2983286 4.6547001 
-Criterion:
-Kolmogorov distance 
-              1e+20 
-> 
-> ## Total variation minimum distance estimator
-> ## Note: you can also use function MDEstimator!
-> ## discretize Gamma distribution
-> MCEstimator(x = x, ParamFamily = G, criterion = TotalVarDist, 
-+             crit.name = "Total variation distance")
-Evaluations of Minimum Total variation distance estimate:
----------------------------------------------------------
-An object of class “Estimate” 
-generated by call
-  MCEstimator(x = x, ParamFamily = G, criterion = TotalVarDist, 
-    crit.name = "Total variation distance")
-samplesize:   50
-estimate:
-    scale     shape 
-0.2829687 5.0197306 
-Criterion:
-Total variation distance 
-               0.4866141 
-> 
-> ## or smooth empirical distribution (takes some time!)
-> #MCEstimator(x = x, ParamFamily = G, criterion = TotalVarDist, 
-> #            asis.smooth.discretize = "smooth", crit.name = "Total variation distance")
-> 
-> ## Hellinger minimum distance estimator
-> ## Note: you can also use function MDEstimator!
-> ## discretize Gamma distribution
-> distroptions(DistrResolution = 1e-8)
-> MCEstimator(x = x, ParamFamily = G, criterion = HellingerDist, 
-+             crit.name = "Hellinger Distance", startPar = c(1,2))
-Evaluations of Minimum Hellinger Distance estimate:
----------------------------------------------------
-An object of class “Estimate” 
-generated by call
-  MCEstimator(x = x, ParamFamily = G, criterion = HellingerDist, 
-    crit.name = "Hellinger Distance", startPar = c(1, 2))
-samplesize:   50
-estimate:
-   scale    shape 
-1.057442 1.683644 
-Criterion:
-Hellinger Distance 
-         0.3782642 
-> distroptions(DistrResolution = 1e-6)
-> 
-> ## or smooth empirical distribution (takes some time!)
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/distr -r 1197


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