[Distr-commits] r1090 - in pkg: distrEx distrEx/man distrEx/tests/Examples utils
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Apr 23 14:00:03 CEST 2016
Author: ruckdeschel
Date: 2016-04-23 14:00:02 +0200 (Sat, 23 Apr 2016)
New Revision: 1090
Added:
pkg/distrEx/tests/Examples/distrEx-Ex_i386.Rout.save
pkg/distrEx/tests/Examples/distrEx-Ex_x64.Rout.save
pkg/utils/RCheckOT.bat
Removed:
pkg/distrEx/tests/Examples/distrEx-Ex.Rout.save
Modified:
pkg/distrEx/DESCRIPTION
pkg/distrEx/man/0distrEx-package.Rd
Log:
distrEx-2.6 is ready for CRAN; updated some utilities;
Modified: pkg/distrEx/DESCRIPTION
===================================================================
--- pkg/distrEx/DESCRIPTION 2016-04-23 11:46:22 UTC (rev 1089)
+++ pkg/distrEx/DESCRIPTION 2016-04-23 12:00:02 UTC (rev 1090)
@@ -1,6 +1,6 @@
Package: distrEx
Version: 2.6
-Date: 2015-11-07
+Date: 2016-04-23
Title: Extensions of Package 'distr'
Description: Extends package 'distr' by functionals, distances, and conditional distributions.
Depends: R(>= 2.10.0), methods, distr(>= 2.2)
@@ -15,4 +15,4 @@
URL: http://distr.r-forge.r-project.org/
LastChangedDate: {$LastChangedDate$}
LastChangedRevision: {$LastChangedRevision$}
-SVNRevision: 1080
+SVNRevision: 1089
Modified: pkg/distrEx/man/0distrEx-package.Rd
===================================================================
--- pkg/distrEx/man/0distrEx-package.Rd 2016-04-23 11:46:22 UTC (rev 1089)
+++ pkg/distrEx/man/0distrEx-package.Rd 2016-04-23 12:00:02 UTC (rev 1090)
@@ -28,14 +28,14 @@
\tabular{ll}{
Package: \tab distrEx \cr
Version: \tab 2.6 \cr
-Date: \tab 2015-11-07 \cr
+Date: \tab 2016-04-23 \cr
Depends: \tab R(>= 2.10.0), methods, distr(>= 2.2) \cr
Imports: \tab startupmsg, utils, stats \cr
Suggests: \tab tcltk \cr
LazyLoad: \tab yes \cr
License: \tab LGPL-3 \cr
URL: \tab http://distr.r-forge.r-project.org/\cr
-SVNRevision: \tab 1080 \cr
+SVNRevision: \tab 1089 \cr
}
}
\section{Classes}{
Deleted: pkg/distrEx/tests/Examples/distrEx-Ex.Rout.save
===================================================================
--- pkg/distrEx/tests/Examples/distrEx-Ex.Rout.save 2016-04-23 11:46:22 UTC (rev 1089)
+++ pkg/distrEx/tests/Examples/distrEx-Ex.Rout.save 2016-04-23 12:00:02 UTC (rev 1090)
@@ -1,1675 +0,0 @@
-
-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 <- "distrEx"
-> source(file.path(R.home("share"), "R", "examples-header.R"))
-> options(warn = 1)
-> library('distrEx')
-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
-
-: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
-
->
-> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
-> cleanEx()
-> nameEx("AbscontCondDistribution-class")
-> ### * AbscontCondDistribution-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: AbscontCondDistribution-class
-> ### Title: Absolutely continuous conditional distribution
-> ### Aliases: AbscontCondDistribution-class
-> ### Keywords: distribution
->
-> ### ** Examples
-> new("AbscontCondDistribution")
-Distribution object of class: AbscontCondDistribution
-## cond:
-An object of class "Condition"
-Slot "name":
-[1] "a condition"
-
->
->
-> cleanEx()
-> nameEx("AsymTotalVarDist")
-> ### * AsymTotalVarDist
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: AsymTotalVarDist
-> ### Title: Generic function for the computation of asymmetric total
-> ### variation distance of two distributions
-> ### Aliases: AsymTotalVarDist AsymTotalVarDist-methods
-> ### AsymTotalVarDist,AbscontDistribution,AbscontDistribution-method
-> ### AsymTotalVarDist,AbscontDistribution,DiscreteDistribution-method
-> ### AsymTotalVarDist,DiscreteDistribution,DiscreteDistribution-method
-> ### AsymTotalVarDist,DiscreteDistribution,AbscontDistribution-method
-> ### AsymTotalVarDist,LatticeDistribution,DiscreteDistribution-method
-> ### AsymTotalVarDist,DiscreteDistribution,LatticeDistribution-method
-> ### AsymTotalVarDist,LatticeDistribution,LatticeDistribution-method
-> ### AsymTotalVarDist,numeric,DiscreteDistribution-method
-> ### AsymTotalVarDist,DiscreteDistribution,numeric-method
-> ### AsymTotalVarDist,numeric,AbscontDistribution-method
-> ### AsymTotalVarDist,AbscontDistribution,numeric-method
-> ### AsymTotalVarDist,AcDcLcDistribution,AcDcLcDistribution-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> AsymTotalVarDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
-+ mixCoeff=c(0.2,0.8)), rho=0.3)
-asym. total variation distance
- 0.5311789
-> AsymTotalVarDist(Norm(), Td(10), rho=0.3)
-asym. total variation distance
- 0.03412602
-> AsymTotalVarDist(Norm(mean = 50, sd = sqrt(25)), Binom(size = 100), rho=0.3) # mutually singular
-asym. total variation distance
- 1
-> AsymTotalVarDist(Pois(10), Binom(size = 20), rho=0.3)
-asym. total variation distance
- 0.3093959
->
-> x <- rnorm(100)
-> AsymTotalVarDist(Norm(), x, rho=0.3)
-asym. total variation distance
- 0.3140162
-> AsymTotalVarDist(x, Norm(), asis.smooth.discretize = "smooth", rho=0.3)
-asym. total variation distance
- 0.2658876
->
-> y <- (rbinom(50, size = 20, prob = 0.5)-10)/sqrt(5)
-> AsymTotalVarDist(y, Norm(), rho=0.3)
-asym. total variation distance
- 0.8343428
-> AsymTotalVarDist(y, Norm(), asis.smooth.discretize = "smooth", rho=0.3)
-asym. total variation distance
- 0.6326863
->
-> AsymTotalVarDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5), rho=0.3)
-asym. total variation distance
- 0.292515
->
->
->
-> cleanEx()
-> nameEx("Condition-class")
-> ### * Condition-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: Condition-class
-> ### Title: Conditions
-> ### Aliases: Condition-class name,Condition-method name<-,Condition-method
-> ### Keywords: distribution
->
-> ### ** Examples
-> new("Condition")
-An object of class "Condition"
-Slot "name":
-[1] "a condition"
-
->
->
-> cleanEx()
-> nameEx("ContaminationSize")
-> ### * ContaminationSize
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: ContaminationSize
-> ### Title: Generic Function for the Computation of the Convex Contamination
-> ### (Pseudo-)Distance of Two Distributions
-> ### Aliases: ContaminationSize ContaminationSize-methods
-> ### ContaminationSize,AbscontDistribution,AbscontDistribution-method
-> ### ContaminationSize,DiscreteDistribution,DiscreteDistribution-method
-> ### ContaminationSize,LatticeDistribution,DiscreteDistribution-method
-> ### ContaminationSize,DiscreteDistribution,LatticeDistribution-method
-> ### ContaminationSize,LatticeDistribution,LatticeDistribution-method
-> ### ContaminationSize,AcDcLcDistribution,AcDcLcDistribution-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> ContaminationSize(Norm(), Norm(mean=0.1))
-$e1
-Distribution Object of Class: Norm
- mean: 0
- sd: 1
-
-$e2
-Distribution Object of Class: Norm
- mean: 0.1
- sd: 1
-
-$size.of.contamination
-[1] 0.3504588
-
-> ContaminationSize(Pois(), Pois(1.5))
-$e1
-Distribution Object of Class: Pois
- lambda: 1
-
-$e2
-Distribution Object of Class: Pois
- lambda: 1.5
-
-$size.of.contamination
-[1] 0.3934693
-
->
->
->
-> cleanEx()
-> nameEx("ConvexContamination")
-> ### * ConvexContamination
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: ConvexContamination
-> ### Title: Generic Function for Generating Convex Contaminations
-> ### Aliases: ConvexContamination ConvexContamination-methods
-> ### ConvexContamination,UnivariateDistribution,UnivariateDistribution,numeric-method
-> ### ConvexContamination,AbscontDistribution,AbscontDistribution,numeric-method
-> ### ConvexContamination,AbscontDistribution,UnivariateDistribution,numeric-method
-> ### ConvexContamination,DiscreteDistribution,DiscreteDistribution,numeric-method
-> ### ConvexContamination,LatticeDistribution,DiscreteDistribution,numeric-method
-> ### ConvexContamination,DiscreteDistribution,LatticeDistribution,numeric-method
-> ### ConvexContamination,LatticeDistribution,LatticeDistribution,numeric-method
-> ### ConvexContamination,AcDcLcDistribution,AcDcLcDistribution,numeric-method
-> ### Keywords: distribution methods
->
-> ### ** Examples
->
-> # Convex combination of two normal distributions
-> C1 <- ConvexContamination(e1 = Norm(), e2 = Norm(mean = 5), size = 0.1)
-> plot(C1)
->
->
->
-> cleanEx()
-> nameEx("CvMDist")
-> ### * CvMDist
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: CvMDist
-> ### Title: Generic function for the computation of the Cramer - von Mises
-> ### distance of two distributions
-> ### Aliases: CvMDist CvMDist-methods
-> ### CvMDist,UnivariateDistribution,UnivariateDistribution-method
-> ### CvMDist,numeric,UnivariateDistribution-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> CvMDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
-+ mixCoeff=c(0.2,0.8)))
-CvM distance
- 0.1812994
-> CvMDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
-+ mixCoeff=c(0.2,0.8)),mu=Norm())
-CvM distance
- 0.1812994
-> CvMDist(Norm(), Td(10))
-CvM distance
- 0.009330691
-> CvMDist(Norm(mean = 50, sd = sqrt(25)), Binom(size = 100))
-CvM distance
- 0.01746156
-> CvMDist(Pois(10), Binom(size = 20))
-CvM distance
- 0.06107322
-> CvMDist(rnorm(100),Norm())
-CvM distance
- 0.04308361
-> CvMDist((rbinom(50, size = 20, prob = 0.5)-10)/sqrt(5), Norm())
-CvM distance
- 0.123967
-> CvMDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5))
-CvM distance
- 0.1307281
-> CvMDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5), mu = Pois())
-CvM distance
- 0.001969063
->
->
->
-> cleanEx()
-> nameEx("DiscreteCondDistribution-class")
-> ### * DiscreteCondDistribution-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: DiscreteCondDistribution-class
-> ### Title: Discrete conditional distribution
-> ### Aliases: DiscreteCondDistribution-class
-> ### Keywords: distribution
->
-> ### ** Examples
-> new("DiscreteCondDistribution")
-Distribution object of class: DiscreteCondDistribution
-## cond:
-An object of class "Condition"
-Slot "name":
-[1] "a condition"
-
->
->
-> cleanEx()
-> nameEx("DiscreteMVDistribution-class")
-> ### * DiscreteMVDistribution-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: DiscreteMVDistribution-class
-> ### Title: Discrete Multivariate Distributions
-> ### Aliases: DiscreteMVDistribution-class
-> ### support,DiscreteMVDistribution-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> (D1 <- new("MultivariateDistribution")) # Dirac measure in (0,0)
-> r(D1)(5)
- [,1] [,2]
-[1,] 0 0
-[2,] 0 0
-[3,] 0 0
-[4,] 0 0
-[5,] 0 0
->
-> (D2 <- DiscreteMVDistribution(supp = matrix(c(1:5, rep(3, 5)), ncol=2, byrow=TRUE)))
-Warning in DiscreteMVDistribution(supp = matrix(c(1:5, rep(3, 5)), ncol = 2, :
- collapsing to unique support values
-> support(D2)
- [,1] [,2]
-[1,] 1 2
-[2,] 3 4
-[3,] 5 3
-[4,] 3 3
-> r(D2)(10)
- [,1] [,2]
- [1,] 3 3
- [2,] 3 3
- [3,] 5 3
- [4,] 1 2
- [5,] 3 3
- [6,] 1 2
- [7,] 1 2
- [8,] 3 4
- [9,] 3 4
-[10,] 3 3
-> d(D2)(support(D2))
-[1] 0.2 0.2 0.2 0.4
-> p(D2)(lower = c(1,1), upper = c(3,3))
-[1] 0.6
-> q(D2)
-NULL
-> param(D2)
-NULL
-> img(D2)
-An object of class "EuclideanSpace"
-Slot "dimension":
-[1] 2
-
-Slot "name":
-[1] "Euclidean Space"
-
->
-> e1 <- E(D2) # expectation
->
->
->
-> cleanEx()
-> nameEx("DiscreteMVDistribution")
-> ### * DiscreteMVDistribution
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: DiscreteMVDistribution
-> ### Title: Generating function for multivariate discrete distribution
-> ### Aliases: DiscreteMVDistribution
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> # Dirac-measure at (0,0,0)
-> D1 <- DiscreteMVDistribution(supp = c(0,0,0))
-> support(D1)
- [,1] [,2] [,3]
-[1,] 0 0 0
->
-> # simple discrete distribution
-> D2 <- DiscreteMVDistribution(supp = matrix(c(0,1,0,2,2,1,1,0), ncol=2),
-+ prob = c(0.3, 0.2, 0.2, 0.3))
-> support(D2)
- [,1] [,2]
-[1,] 0 2
-[2,] 1 1
-[3,] 0 1
-[4,] 2 0
-> r(D2)(10)
- [,1] [,2]
- [1,] 0 2
- [2,] 2 0
- [3,] 2 0
- [4,] 1 1
- [5,] 0 2
- [6,] 1 1
- [7,] 1 1
- [8,] 0 1
- [9,] 0 1
-[10,] 0 2
->
->
->
-> cleanEx()
-> nameEx("E")
-> ### * E
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: E
-> ### Title: Generic Function for the Computation of (Conditional)
-> ### Expectations
-> ### Aliases: E E-methods E,UnivariateDistribution,missing,missing-method
-> ### E,AbscontDistribution,missing,missing-method
-> ### E,DiscreteDistribution,missing,missing-method
-> ### E,LatticeDistribution,missing,missing-method
-> ### E,AffLinDistribution,missing,missing-method
-> ### E,AffLinAbscontDistribution,missing,missing-method
-> ### E,AffLinDiscreteDistribution,missing,missing-method
-> ### E,AffLinLatticeDistribution,missing,missing-method
-> ### E,MultivariateDistribution,missing,missing-method
-> ### E,DiscreteMVDistribution,missing,missing-method
-> ### E,UnivarLebDecDistribution,missing,missing-method
-> ### E,AffLinUnivarLebDecDistribution,missing,missing-method
-> ### E,UnivarMixingDistribution,missing,missing-method
-> ### E,UnivariateDistribution,function,missing-method
-> ### E,AbscontDistribution,function,missing-method
-> ### E,DiscreteDistribution,function,missing-method
-> ### E,LatticeDistribution,function,missing-method
-> ### E,MultivariateDistribution,function,missing-method
-> ### E,DiscreteMVDistribution,function,missing-method
-> ### E,UnivarLebDecDistribution,function,missing-method
-> ### E,UnivarMixingDistribution,function,missing-method
-> ### E,AcDcLcDistribution,ANY,ANY-method
-> ### E,CompoundDistribution,missing,missing-method
-> ### E,UnivariateCondDistribution,missing,numeric-method
-> ### E,AbscontCondDistribution,missing,numeric-method
-> ### E,DiscreteCondDistribution,missing,numeric-method
-> ### E,UnivarLebDecDistribution,missing,ANY-method
-> ### E,UnivarMixingDistribution,missing,ANY-method
-> ### E,UnivarLebDecDistribution,function,ANY-method
-> ### E,UnivariateCondDistribution,function,numeric-method
-> ### E,UnivarMixingDistribution,function,ANY-method
-> ### E,AbscontCondDistribution,function,numeric-method
-> ### E,DiscreteCondDistribution,function,numeric-method
-> ### E,Arcsine,missing,missing-method E,Beta,missing,missing-method
-> ### E,Binom,missing,missing-method E,Cauchy,missing,missing-method
-> ### E,Chisq,missing,missing-method E,Dirac,missing,missing-method
-> ### E,DExp,missing,missing-method E,Exp,missing,missing-method
-> ### E,Fd,missing,missing-method E,Gammad,missing,missing-method
-> ### E,Gammad,function,missing-method E,Geom,missing,missing-method
-> ### E,Gumbel,missing,missing-method E,GPareto,missing,missing-method
-> ### E,GPareto,function,missing-method E,GEV,missing,missing-method
-> ### E,GEV,function,missing-method E,Hyper,missing,missing-method
-> ### E,Logis,missing,missing-method E,Lnorm,missing,missing-method
-> ### E,Nbinom,missing,missing-method E,Norm,missing,missing-method
-> ### E,Pareto,missing,missing-method E,Pois,missing,missing-method
-> ### E,Td,missing,missing-method E,Unif,missing,missing-method
-> ### E,Weibull,missing,missing-method
-> ### Keywords: methods distribution
->
-> ### ** Examples
->
-> # mean of Exp(1) distribution
-> E <- Exp()
->
-> E(E) ## uses explicit terms
-[1] 1
-> E(as(E,"AbscontDistribution")) ## uses numerical integration
-[1] 0.9999983
-> E(as(E,"UnivariateDistribution")) ## uses simulations
-[1] 1.002698
-> E(E, fun = function(x){2*x^2}) ## uses simulations
-[1] 3.999941
->
-> # the same operator for discrete distributions:
-> P <- Pois(lambda=2)
->
-> E(P) ## uses explicit terms
-[1] 2
-> E(as(P,"DiscreteDistribution")) ## uses sums
-[1] 1.999997
-> E(as(P,"UnivariateDistribution")) ## uses simulations
-[1] 2.00881
-> E(P, fun = function(x){2*x^2}) ## uses simulations
-[1] 11.99993
->
->
-> # second moment of N(1,4)
-> E(Norm(mean=1, sd=2), fun = function(x){x^2})
-[1] 4.999977
-> E(Norm(mean=1, sd=2), fun = function(x){x^2}, useApply = FALSE)
-[1] 4.999977
->
-> # conditional distribution of a linear model
-> D1 <- LMCondDistribution(theta = 1)
-> E(D1, cond = 1)
-[1] 0.9999998
-> E(Norm(mean=1))
-[1] 1
-> E(D1, function(x){x^2}, cond = 1)
-[1] 1.999994
-> E(Norm(mean=1), fun = function(x){x^2})
-[1] 1.999994
-> E(D1, function(x, cond){cond*x^2}, cond = 2, withCond = TRUE, useApply = FALSE)
-[1] 9.999987
-> E(Norm(mean=2), function(x){2*x^2})
-[1] 9.999987
->
-> E(as(Norm(mean=2),"AbscontDistribution"))
-[1] 2
-> ### somewhat less accurate:
-> E(as(Norm(mean=2),"AbscontDistribution"),
-+ lowerTruncQuantil=1e-4,upperTruncQuantil=1e-4, IQR.fac= 4)
-[1] 2
-> ### even less accurate:
-> E(as(Norm(mean=2),"AbscontDistribution"),
-+ lowerTruncQuantil=1e-2,upperTruncQuantil=1e-2, IQR.fac= 4)
-[1] 2
-> ### no good idea, but just as an example:
-> E(as(Norm(mean=2),"AbscontDistribution"),
-+ lowerTruncQuantil=1e-2,upperTruncQuantil=1e-2, IQR.fac= .1)
-[1] 2
->
-> ### truncation of integration range; see also m1df...
-> E(Norm(mean=2), low=2,upp=4)
-[1] 1.299451
->
-> E(Cauchy())
-[1] NA
-> E(Cauchy(),upp=3,low=-2)
-[1] 0
-> # some Lebesgue decomposed distribution
-> mymix <- UnivarLebDecDistribution(acPart = Norm(), discretePart = Binom(4,.4),
-+ acWeight = 0.4)
-> E(mymix)
-[1] 0.96
->
->
->
-> cleanEx()
-> nameEx("EmpiricalMVDistribution")
-> ### * EmpiricalMVDistribution
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: EmpiricalMVDistribution
-> ### Title: Generating function for mulitvariate discrete distribution
-> ### Aliases: EmpiricalMVDistribution
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> ## generate some data
-> X <- matrix(rnorm(50), ncol = 5)
->
-> ## empirical distribution of X
-> D1 <- EmpiricalMVDistribution(data = X)
-> support(D1)
- [,1] [,2] [,3] [,4] [,5]
- [1,] -0.6264538 1.51178117 0.91897737 1.35867955 -0.1645236
- [2,] 0.1836433 0.38984324 0.78213630 -0.10278773 -0.2533617
- [3,] -0.8356286 -0.62124058 0.07456498 0.38767161 0.6969634
- [4,] 1.5952808 -2.21469989 -1.98935170 -0.05380504 0.5566632
- [5,] 0.3295078 1.12493092 0.61982575 -1.37705956 -0.6887557
- [6,] -0.8204684 -0.04493361 -0.05612874 -0.41499456 -0.7074952
- [7,] 0.4874291 -0.01619026 -0.15579551 -0.39428995 0.3645820
- [8,] 0.7383247 0.94383621 -1.47075238 -0.05931340 0.7685329
- [9,] 0.5757814 0.82122120 -0.47815006 1.10002537 -0.1123462
-[10,] -0.3053884 0.59390132 0.41794156 0.76317575 0.8811077
-> r(D1)(10)
- [,1] [,2] [,3] [,4] [,5]
- [1,] 0.7383247 0.94383621 -1.47075238 -0.05931340 0.7685329
- [2,] 0.3295078 1.12493092 0.61982575 -1.37705956 -0.6887557
- [3,] 1.5952808 -2.21469989 -1.98935170 -0.05380504 0.5566632
- [4,] -0.6264538 1.51178117 0.91897737 1.35867955 -0.1645236
- [5,] 0.7383247 0.94383621 -1.47075238 -0.05931340 0.7685329
- [6,] 1.5952808 -2.21469989 -1.98935170 -0.05380504 0.5566632
- [7,] -0.8356286 -0.62124058 0.07456498 0.38767161 0.6969634
- [8,] -0.8204684 -0.04493361 -0.05612874 -0.41499456 -0.7074952
- [9,] -0.6264538 1.51178117 0.91897737 1.35867955 -0.1645236
-[10,] 0.4874291 -0.01619026 -0.15579551 -0.39428995 0.3645820
->
->
->
-> cleanEx()
-> nameEx("EuclCondition-class")
-> ### * EuclCondition-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: EuclCondition-class
-> ### Title: Conditioning by an Euclidean space.
-> ### Aliases: EuclCondition-class Range Range,EuclCondition-method
-> ### show,EuclCondition-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> new("EuclCondition")
-name: conditioning by an Euclidean space
-Range: Euclidean Space with dimension 1
->
->
->
-> cleanEx()
-> nameEx("EuclCondition")
-> ### * EuclCondition
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: EuclCondition
-> ### Title: Generating function for EuclCondition-class
-> ### Aliases: EuclCondition
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> EuclCondition(dimension = 3)
-name: conditioning by an Euclidean space
-Range: Euclidean Space with dimension 3
->
-> ## The function is currently defined as
-> function(dimension){
-+ new("EuclCondition", Range = EuclideanSpace(dimension = dimension))
-+ }
-function (dimension)
-{
- new("EuclCondition", Range = EuclideanSpace(dimension = dimension))
-}
->
->
->
-> cleanEx()
-> nameEx("GLIntegrate")
-> ### * GLIntegrate
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: GLIntegrate
-> ### Title: Gauss-Legendre Quadrature
-> ### Aliases: GLIntegrate
-> ### Keywords: math utilities
->
-> ### ** Examples
->
-> integrate(dnorm, -1.96, 1.96)
-0.9500042 with absolute error < 1e-11
-> GLIntegrate(dnorm, -1.96, 1.96)
-[1] 0.9500042
->
->
->
-> cleanEx()
-> nameEx("HellingerDist")
-> ### * HellingerDist
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: HellingerDist
-> ### Title: Generic function for the computation of the Hellinger distance
-> ### of two distributions
-> ### Aliases: HellingerDist HellingerDist-methods
-> ### HellingerDist,AbscontDistribution,AbscontDistribution-method
-> ### HellingerDist,AbscontDistribution,DiscreteDistribution-method
-> ### HellingerDist,DiscreteDistribution,DiscreteDistribution-method
-> ### HellingerDist,DiscreteDistribution,AbscontDistribution-method
-> ### HellingerDist,LatticeDistribution,DiscreteDistribution-method
-> ### HellingerDist,DiscreteDistribution,LatticeDistribution-method
-> ### HellingerDist,LatticeDistribution,LatticeDistribution-method
-> ### HellingerDist,numeric,DiscreteDistribution-method
-> ### HellingerDist,DiscreteDistribution,numeric-method
-> ### HellingerDist,numeric,AbscontDistribution-method
-> ### HellingerDist,AbscontDistribution,numeric-method
-> ### HellingerDist,AcDcLcDistribution,AcDcLcDistribution-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> HellingerDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
-+ mixCoeff=c(0.2,0.8)))
-Hellinger distance
- 0.4604849
-> HellingerDist(Norm(), Td(10))
-Hellinger distance
- 0.0598968
-> HellingerDist(Norm(mean = 50, sd = sqrt(25)), Binom(size = 100)) # mutually singular
-Hellinger distance
- 1
-> HellingerDist(Pois(10), Binom(size = 20))
-Hellinger distance
- 0.1742254
->
-> x <- rnorm(100)
-> HellingerDist(Norm(), x)
-Hellinger distance
- 0.3287617
-> HellingerDist(x, Norm(), asis.smooth.discretize = "smooth")
-Hellinger distance
- 0.1904838
->
-> y <- (rbinom(50, size = 20, prob = 0.5)-10)/sqrt(5)
-> HellingerDist(y, Norm())
-Hellinger distance
- 0.7596996
-> HellingerDist(y, Norm(), asis.smooth.discretize = "smooth")
-Hellinger distance
- 0.5283272
->
-> HellingerDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5))
-Hellinger distance
- 0.1945649
->
->
->
-> cleanEx()
-> nameEx("KolmogorovDist")
-> ### * KolmogorovDist
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: KolmogorovDist
-> ### Title: Generic function for the computation of the Kolmogorov distance
-> ### of two distributions
-> ### Aliases: KolmogorovDist KolmogorovDist-methods
-> ### KolmogorovDist,AbscontDistribution,AbscontDistribution-method
-> ### KolmogorovDist,AbscontDistribution,DiscreteDistribution-method
-> ### KolmogorovDist,DiscreteDistribution,DiscreteDistribution-method
-> ### KolmogorovDist,DiscreteDistribution,AbscontDistribution-method
-> ### KolmogorovDist,LatticeDistribution,DiscreteDistribution-method
-> ### KolmogorovDist,DiscreteDistribution,LatticeDistribution-method
-> ### KolmogorovDist,LatticeDistribution,LatticeDistribution-method
-> ### KolmogorovDist,numeric,UnivariateDistribution-method
-> ### KolmogorovDist,UnivariateDistribution,numeric-method
-> ### KolmogorovDist,AcDcLcDistribution,AcDcLcDistribution-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> KolmogorovDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
-+ mixCoeff=c(0.2,0.8)))
-Kolmogorov distance
- 0.3092684
-> KolmogorovDist(Norm(), Td(10))
-Kolmogorov distance
- 0.01554215
-> KolmogorovDist(Norm(mean = 50, sd = sqrt(25)), Binom(size = 100))
-Kolmogorov distance
- 0.03979462
-> KolmogorovDist(Pois(10), Binom(size = 20))
-Kolmogorov distance
- 0.08863266
-> KolmogorovDist(Norm(), rnorm(100))
-Kolmogorov distance
- 0.1149486
-> KolmogorovDist((rbinom(50, size = 20, prob = 0.5)-10)/sqrt(5), Norm())
-Kolmogorov distance
- 0.1673604
-> KolmogorovDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5))
-Kolmogorov distance
- 0.03172234
->
->
->
-> cleanEx()
-> nameEx("LMCondDistribution")
-> ### * LMCondDistribution
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: LMCondDistribution
-> ### Title: Generating function for the conditional distribution of a linear
-> ### regression model.
-> ### Aliases: LMCondDistribution
-> ### Keywords: distribution models
->
-> ### ** Examples
->
-> # normal error distribution
-> (D1 <- LMCondDistribution(theta = 1)) # corresponds to Norm(cond, 1)
-Distribution object of class: AbscontCondDistribution
- theta: 1
- intercept: 0
- scale: 1
-## cond:
-name: conditioning by an Euclidean space
-Range: Euclidean Space with dimension 1
-> plot(D1)
-Warning in plot(D1) :
- 'plot' not yet implemented for objects of class AbscontCondDistribution
-> r(D1)
-function (n, cond, ...)
-{
- if (length(cond) != 1L)
- stop("'cond' has wrong dimension")
- r <- function (n)
- {
- rnorm(n, mean = 0, sd = 1)
- }
- 0 + cond %*% 1 + 1 * r(n, ...)
-}
-<environment: 0x529b0a0>
-> d(D1)
-function (x, cond, log = FALSE, ...)
-{
- if (length(cond) != 1L)
- stop("'cond' has wrong dimension")
- d <- function (x, log = FALSE)
- {
- dnorm(x, mean = 0, sd = 1, log = log)
- }
- if ("log" %in% names(formals(d)))
- d0 <- d((x - 0 - as.vector(cond %*% 1))/1, log = log)
- else {
- d0 <- d((x - 0 - as.vector(cond %*% 1))/1)
- if (log)
- d0 <- log(d0)
- }
- if (log)
- d0 <- d0 - log(1)
- else d0 <- d0/1
- return(d0)
-}
-<environment: 0x529b0a0>
-> p(D1)
-function (q, cond, lower.tail = TRUE, log.p = FALSE, ...)
-{
- if (length(cond) != 1L)
- stop("'cond' has wrong dimension")
- p <- function (q, lower.tail = TRUE, log.p = FALSE)
- {
- pnorm(q, mean = 0, sd = 1, lower.tail = lower.tail, log.p = log.p)
- }
- argList <- alist((q - 0 - as.vector(cond %*% 1))/1)
- if ("lower.tail" %in% names(formals(p)))
- argList <- c(argList, lower.tail = lower.tail)
- if ("log.p" %in% names(formals(p)))
- argList <- c(argList, log.p = log.p)
- dots <- alist(...)
- if (length(dots))
- argList <- c(argList, dots)
- p0 <- do.call(p, argList)
- if (!("lower.tail" %in% names(formals(p))))
- if (!lower.tail)
- p0 <- 1 - p0
- if (!("log.p" %in% names(formals(p))))
- if (log.p)
- p0 <- log(p0)
- return(p0)
-}
-<environment: 0x529b0a0>
-> q(D1)
-function (p, cond, lower.tail = TRUE, log.p = FALSE, ...)
-{
- if (length(cond) != 1L)
- stop("'cond' has wrong dimension")
- q <- function (p, lower.tail = TRUE, log.p = FALSE)
- {
- qnorm(p, mean = 0, sd = 1, lower.tail = lower.tail, log.p = log.p)
- }
- argList <- alist(p)
- if ("lower.tail" %in% names(formals(q)))
- argList <- c(argList, lower.tail = lower.tail)
- else if (log.p)
- p <- exp(p)
- if ("log.p" %in% names(formals(q)))
- argList <- c(argList, log.p = log.p)
- else if (!lower.tail)
- p <- 1 - p
- dots <- alist(...)
- if (length(dots))
- argList <- c(argList, dots)
- 1 * do.call(q, argList) + 0 + as.vector(cond %*% 1)
-}
-<environment: 0x529b0a0>
-> param(D1)
-name: parameter of a linear regression model
-theta: 1
-intercept: 0
-scale: 1
-> cond(D1)
-name: conditioning by an Euclidean space
-Range: Euclidean Space with dimension 1
->
-> d(D1)(0, cond = 1)
-[1] 0.2419707
-> d(Norm(mean=1))(0)
-[1] 0.2419707
->
-> E(D1, cond = 1)
-[1] 0.9999998
-> E(D1, function(x){x^2}, cond = 2)
-[1] 4.999993
-> E(Norm(mean=2), function(x){x^2})
-[1] 4.999993
->
->
->
-> cleanEx()
-> nameEx("LMParameter-class")
-> ### * LMParameter-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: LMParameter-class
-> ### Title: Parameter of a linear regression model
-> ### Aliases: LMParameter-class show,LMParameter-method
-> ### Keywords: distribution
->
-> ### ** Examples
->
-> new("LMParameter")
-name: parameter of a linear regression model
-theta: 0
-intercept: 0
-scale: 1
->
->
->
-> cleanEx()
-> nameEx("LMParameter")
-> ### * LMParameter
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: LMParameter
-> ### Title: Generating function for LMParameter-class
-> ### Aliases: LMParameter
-> ### Keywords: models
->
-> ### ** Examples
->
-> LMParameter(theta = c(1,1), intercept = 2, scale = 0.5)
-name: parameter of a linear regression model
-theta: 1
- theta: 1
-intercept: 2
-scale: 0.5
->
-> ## The function is currently defined as
-> function(theta = 0, intercept = 0, scale = 1){
-+ new("LMParameter", theta = theta, intercept = intercept, scale = 1)
-+ }
-function (theta = 0, intercept = 0, scale = 1)
-{
- new("LMParameter", theta = theta, intercept = intercept,
- scale = 1)
-}
->
->
->
-> cleanEx()
-> nameEx("MultivariateDistribution-class")
-> ### * MultivariateDistribution-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: MultivariateDistribution-class
-> ### Title: Multivariate Distributions
-> ### Aliases: MultivariateDistribution-class
-> ### show,MultivariateDistribution-method
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/distr -r 1090
More information about the Distr-commits
mailing list