[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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