[Vinecopula-commits] r65 - in pkg: man tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Di Apr 22 17:23:47 CEST 2014
Author: etobi
Date: 2014-04-22 17:23:46 +0200 (Tue, 22 Apr 2014)
New Revision: 65
Modified:
pkg/man/RVineStructureSelect.Rd
pkg/man/RVineTreePlot.Rd
pkg/tests/Examples/VineCopula-Ex.Rout.save
Log:
Examples f?\195?\188r RVineStructureSelect und RVineTreePlot angepasst.
Modified: pkg/man/RVineStructureSelect.Rd
===================================================================
--- pkg/man/RVineStructureSelect.Rd 2014-04-22 13:49:04 UTC (rev 64)
+++ pkg/man/RVineStructureSelect.Rd 2014-04-22 15:23:46 UTC (rev 65)
@@ -118,9 +118,7 @@
data(daxreturns)
# select the R-vine structure, families and parameters
-\dontrun{
-RVM = RVineStructureSelect(daxreturns,c(1:6),progress=TRUE)
-}
+RVM = RVineStructureSelect(daxreturns[,1:4],c(1:6),progress=TRUE)
# specify a C-vine copula model with only Clayton, Gumbel and Frank copulas
\dontrun{
Modified: pkg/man/RVineTreePlot.Rd
===================================================================
--- pkg/man/RVineTreePlot.Rd 2014-04-22 13:49:04 UTC (rev 64)
+++ pkg/man/RVineTreePlot.Rd 2014-04-22 15:23:46 UTC (rev 65)
@@ -89,6 +89,6 @@
# re-set random seed for testing
set.seed(666)
# plot only the first tree with new coordinates
-RVineTreePlot(data=NULL,RVM=RVM,tree=1,edge.labels=FALSE,P=P)
+P = RVineTreePlot(data=NULL,RVM=RVM,tree=1,edge.labels=FALSE,P=P)
}
Modified: pkg/tests/Examples/VineCopula-Ex.Rout.save
===================================================================
--- pkg/tests/Examples/VineCopula-Ex.Rout.save 2014-04-22 13:49:04 UTC (rev 64)
+++ pkg/tests/Examples/VineCopula-Ex.Rout.save 2014-04-22 15:23:46 UTC (rev 65)
@@ -1,7 +1,7 @@
-R version 3.1.0 alpha (2014-03-13 r65184) -- "Unsuffered Consequences"
+R version 3.0.3 (2014-03-06) -- "Warm Puppy"
Copyright (C) 2014 The R Foundation for Statistical Computing
-Platform: x86_64-w64-mingw32/x64 (64-bit)
+Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
@@ -22,3355 +22,3330 @@
> options(warn = 1)
> options(pager = "console")
> library('VineCopula')
->
-> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
-> cleanEx()
-> nameEx("BB1Copula-class")
-> ### * BB1Copula-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB1Copula-class
-> ### Title: Classes '"BB1Copula"', '"surBB1Copula"', '"r90BB1Copula"' and
-> ### '"r270BB1Copula"'
-> ### Aliases: BB1Copula-class dduCopula,numeric,BB1Copula-method
-> ### ddvCopula,numeric,BB1Copula-method dduCopula,matrix,BB1Copula-method
-> ### ddvCopula,matrix,BB1Copula-method getKendallDistr,BB1Copula-method
-> ### kendallDistribution,BB1Copula-method surBB1Copula-class
-> ### dduCopula,numeric,surBB1Copula-method
-> ### ddvCopula,numeric,surBB1Copula-method
-> ### dduCopula,matrix,surBB1Copula-method
-> ### ddvCopula,matrix,surBB1Copula-method r90BB1Copula-class
-> ### dduCopula,numeric,r90BB1Copula-method
-> ### ddvCopula,numeric,r90BB1Copula-method
-> ### dduCopula,matrix,r90BB1Copula-method
-> ### ddvCopula,matrix,r90BB1Copula-method r270BB1Copula-class
-> ### dduCopula,numeric,r270BB1Copula-method
-> ### ddvCopula,numeric,r270BB1Copula-method
-> ### dduCopula,matrix,r270BB1Copula-method
-> ### ddvCopula,matrix,r270BB1Copula-method
-> ### Keywords: classes
->
-> ### ** Examples
->
-> showClass("BB1Copula")
-Class "BB1Copula" [package "VineCopula"]
-
-Slots:
-
-Name: family dimension parameters param.names param.lowbnd
-Class: numeric integer numeric character numeric
-
-Name: param.upbnd fullname
-Class: numeric character
-
-Extends:
-Class "copula", directly
-Class "twoParamBiCop", directly
-Class "Copula", by class "copula", distance 2
->
->
->
-> cleanEx()
-> nameEx("BB1Copula")
-> ### * BB1Copula
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB1Copula
-> ### Title: Constructor of the BB1 family and rotated versions thereof
-> ### Aliases: BB1Copula surBB1Copula r90BB1Copula r270BB1Copula
-> ### Keywords: distribution copula
->
-> ### ** Examples
->
-> library(copula)
-
-Attaching package: 'copula'
-
-The following object is masked from 'package:VineCopula':
-
- fitCopula
-
->
-> persp(BB1Copula(c(1,1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(surBB1Copula(c(1,1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(r90BB1Copula(c(-1,-1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(r270BB1Copula(c(-1,-1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
->
->
->
-> cleanEx()
-
-detaching 'package:copula'
-
-> nameEx("BB6Copula-class")
-> ### * BB6Copula-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB6Copula-class
-> ### Title: Classes '"BB6Copula"', '"surBB6Copula"', '"r90BB6Copula"' and
-> ### '"r270BB6Copula"'
-> ### Aliases: BB6Copula-class dduCopula,numeric,BB6Copula-method
-> ### ddvCopula,numeric,BB6Copula-method dduCopula,matrix,BB6Copula-method
-> ### ddvCopula,matrix,BB6Copula-method getKendallDistr,BB6Copula-method
-> ### kendallDistribution,BB6Copula-method surBB6Copula-class
-> ### dduCopula,numeric,surBB6Copula-method
-> ### ddvCopula,numeric,surBB6Copula-method
-> ### dduCopula,matrix,surBB6Copula-method
-> ### ddvCopula,matrix,surBB6Copula-method r90BB6Copula-class
-> ### dduCopula,numeric,r90BB6Copula-method
-> ### ddvCopula,numeric,r90BB6Copula-method
-> ### dduCopula,matrix,r90BB6Copula-method
-> ### ddvCopula,matrix,r90BB6Copula-method r270BB6Copula-class
-> ### dduCopula,numeric,r270BB6Copula-method
-> ### ddvCopula,numeric,r270BB6Copula-method
-> ### dduCopula,matrix,r270BB6Copula-method
-> ### ddvCopula,matrix,r270BB6Copula-method
-> ### Keywords: classes
->
-> ### ** Examples
->
-> showClass("BB6Copula")
-Class "BB6Copula" [package "VineCopula"]
-
-Slots:
-
-Name: family dimension parameters param.names param.lowbnd
-Class: numeric integer numeric character numeric
-
-Name: param.upbnd fullname
-Class: numeric character
-
-Extends:
-Class "copula", directly
-Class "twoParamBiCop", directly
-Class "Copula", by class "copula", distance 2
->
->
->
-> cleanEx()
-> nameEx("BB6Copula")
-> ### * BB6Copula
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB6Copula
-> ### Title: Constructor of the BB6 family and its derivatives
-> ### Aliases: BB6Copula surBB6Copula r90BB6Copula r270BB6Copula
->
-> ### ** Examples
->
-> library(copula)
-
-Attaching package: 'copula'
-
-The following object is masked from 'package:VineCopula':
-
- fitCopula
-
->
-> persp(BB6Copula(c(1,1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(surBB6Copula(c(1,1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(r90BB6Copula(c(-1,-1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(r270BB6Copula(c(-1,-1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
->
->
->
-> cleanEx()
-
-detaching 'package:copula'
-
-> nameEx("BB7Copula-class")
-> ### * BB7Copula-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB7Copula-class
-> ### Title: Classes '"BB7Copula"', '"surBB7Copula"', '"r90BB7Copula"' and
-> ### '"r270BB7Copula"'
-> ### Aliases: BB7Copula-class dduCopula,numeric,BB7Copula-method
-> ### ddvCopula,numeric,BB7Copula-method dduCopula,matrix,BB7Copula-method
-> ### ddvCopula,matrix,BB7Copula-method getKendallDistr,BB7Copula-method
-> ### kendallDistribution,BB7Copula-method surBB7Copula-class
-> ### dduCopula,numeric,surBB7Copula-method
-> ### ddvCopula,numeric,surBB7Copula-method
-> ### dduCopula,matrix,surBB7Copula-method
-> ### ddvCopula,matrix,surBB7Copula-method r90BB7Copula-class
-> ### dduCopula,numeric,r90BB7Copula-method
-> ### ddvCopula,numeric,r90BB7Copula-method
-> ### dduCopula,matrix,r90BB7Copula-method
-> ### ddvCopula,matrix,r90BB7Copula-method r270BB7Copula-class
-> ### dduCopula,numeric,r270BB7Copula-method
-> ### ddvCopula,numeric,r270BB7Copula-method
-> ### dduCopula,matrix,r270BB7Copula-method
-> ### ddvCopula,matrix,r270BB7Copula-method
-> ### Keywords: classes
->
-> ### ** Examples
->
-> showClass("BB7Copula")
-Class "BB7Copula" [package "VineCopula"]
-
-Slots:
-
-Name: family dimension parameters param.names param.lowbnd
-Class: numeric integer numeric character numeric
-
-Name: param.upbnd fullname
-Class: numeric character
-
-Extends:
-Class "copula", directly
-Class "twoParamBiCop", directly
-Class "Copula", by class "copula", distance 2
->
->
->
-> cleanEx()
-> nameEx("BB7Copula")
-> ### * BB7Copula
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB7Copula
-> ### Title: Constructor of the BB7 family and its derivatives
-> ### Aliases: BB7Copula surBB7Copula r90BB7Copula r270BB7Copula
->
-> ### ** Examples
->
-> library(copula)
-
-Attaching package: 'copula'
-
-The following object is masked from 'package:VineCopula':
-
- fitCopula
-
->
-> persp(BB7Copula(c(1,1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(surBB7Copula(c(1,1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(r90BB7Copula(c(-1,-1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
-> persp(r270BB7Copula(c(-1,-1.5)),dCopula, zlim=c(0,10))
-Warning in persp.default(xis, yis, zmat, theta = theta, phi = phi, expand = expand, :
- surface extends beyond the box
->
->
->
-> cleanEx()
-
-detaching 'package:copula'
-
-> nameEx("BB8Copula-class")
-> ### * BB8Copula-class
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB8Copula-class
-> ### Title: Classes '"BB8Copula"', '"surBB8Copula"', '"r90BB8Copula"' and
-> ### '"r270BB8Copula"'
-> ### Aliases: BB8Copula-class dduCopula,numeric,BB8Copula-method
-> ### ddvCopula,numeric,BB8Copula-method dduCopula,matrix,BB8Copula-method
-> ### ddvCopula,matrix,BB8Copula-method getKendallDistr,BB8Copula-method
-> ### kendallDistribution,BB8Copula-method surBB8Copula-class
-> ### dduCopula,numeric,surBB8Copula-method
-> ### ddvCopula,numeric,surBB8Copula-method
-> ### dduCopula,matrix,surBB8Copula-method
-> ### ddvCopula,matrix,surBB8Copula-method r90BB8Copula-class
-> ### dduCopula,numeric,r90BB8Copula-method
-> ### ddvCopula,numeric,r90BB8Copula-method
-> ### dduCopula,matrix,r90BB8Copula-method
-> ### ddvCopula,matrix,r90BB8Copula-method r270BB8Copula-class
-> ### dduCopula,numeric,r270BB8Copula-method
-> ### ddvCopula,numeric,r270BB8Copula-method
-> ### dduCopula,matrix,r270BB8Copula-method
-> ### ddvCopula,matrix,r270BB8Copula-method fitCopula,twoParamBiCop-method
-> ### Keywords: classes
->
-> ### ** Examples
->
-> showClass("BB8Copula")
-Class "BB8Copula" [package "VineCopula"]
-
-Slots:
-
-Name: family dimension parameters param.names param.lowbnd
-Class: numeric integer numeric character numeric
-
-Name: param.upbnd fullname
-Class: numeric character
-
-Extends:
-Class "copula", directly
-Class "twoParamBiCop", directly
-Class "Copula", by class "copula", distance 2
->
->
->
-> cleanEx()
-> nameEx("BB8Copula")
-> ### * BB8Copula
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BB8Copula
-> ### Title: Constructor of the BB8 family and its derivatives
-> ### Aliases: BB8Copula surBB8Copula r90BB8Copula r270BB8Copula
->
-> ### ** Examples
->
-> library(copula)
-
-Attaching package: 'copula'
-
-The following object is masked from 'package:VineCopula':
-
- fitCopula
-
->
-> persp(BB8Copula(c(1,0.5)),dCopula, zlim=c(0,10))
-> persp(surBB8Copula(c(1,0.5)),dCopula, zlim=c(0,10))
-> persp(r90BB8Copula(c(-1,-0.5)),dCopula, zlim=c(0,10))
-> persp(r270BB8Copula(c(-1,-0.5)),dCopula, zlim=c(0,10))
->
->
->
-> cleanEx()
-
-detaching 'package:copula'
-
-> nameEx("BetaMatrix")
-> ### * BetaMatrix
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BetaMatrix
-> ### Title: Matrix of empirical Blomqvist's beta values
-> ### Aliases: BetaMatrix
->
-> ### ** Examples
->
-> data(daxreturns)
-> Data = as.matrix(daxreturns)
->
-> # compute the empirical Blomqvist's betas
-> beta = BetaMatrix(Data)
->
->
->
-> cleanEx()
-> nameEx("BiCopCDF")
-> ### * BiCopCDF
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopCDF
-> ### Title: Distribution function of a bivariate copula
-> ### Aliases: BiCopCDF
->
-> ### ** Examples
->
-> # simulate from a bivariate Clayton
-> simdata = BiCopSim(300,3,3.4)
->
-> # evaluate the distribution function of the bivariate t-copula
-> u1 = simdata[,1]
-> u2 = simdata[,2]
-> BiCopCDF(u1,u2,3,3.4)
- [1] 0.212083894 0.560454175 0.196829776 0.859783373 0.334142797 0.138883599
- [7] 0.552748610 0.656946696 0.716294284 0.358902123 0.657106132 0.406649144
- [13] 0.215249319 0.010762322 0.680744432 0.429168263 0.336836485 0.754999947
- [19] 0.478876720 0.591381592 0.743614123 0.684320242 0.501994536 0.019720627
- [25] 0.673692866 0.461672664 0.318172610 0.041830170 0.272430716 0.539624409
- [31] 0.690952756 0.357404390 0.478385703 0.450454976 0.081735412 0.325850276
- [37] 0.270163818 0.464160879 0.697817469 0.770254930 0.402432651 0.309902564
- [43] 0.526755239 0.440604149 0.157859951 0.125915290 0.623294168 0.739830600
- [49] 0.371783278 0.723334913 0.516814728 0.269809505 0.445858863 0.109397786
- [55] 0.822399359 0.909295094 0.294693765 0.055312093 0.427042357 0.403252652
- [61] 0.845573559 0.325266686 0.630782109 0.357571974 0.203263111 0.321058009
- [67] 0.032144353 0.826230667 0.484706552 0.844421805 0.608301620 0.175590037
- [73] 0.609058333 0.163878464 0.101571465 0.538136460 0.274644551 0.339289595
- [79] 0.294216757 0.195381304 0.277706814 0.421733480 0.720333595 0.049782599
- [85] 0.565454106 0.606022665 0.691741950 0.371062856 0.601897391 0.591440556
- [91] 0.201688601 0.758395667 0.600742911 0.704561061 0.823026172 0.574257107
- [97] 0.099157544 0.251281107 0.106087540 0.300755602 0.189355553 0.383442247
-[103] 0.156051138 0.353445260 0.237769014 0.569868697 0.754009708 0.626534957
-[109] 0.642565610 0.746343146 0.174386590 0.276468326 0.793197597 0.128246949
-[115] 0.403133907 0.218942785 0.337329582 0.358487519 0.603040975 0.358813218
-[121] 0.270584669 0.584756888 0.291400991 0.270925282 0.453995540 0.750041775
-[127] 0.419655276 0.292947806 0.211477158 0.367187001 0.403616732 0.263279705
-[133] 0.466439291 0.060463864 0.414509798 0.037596575 0.254942295 0.205299452
-[139] 0.174436459 0.449884240 0.024025747 0.703587274 0.030608188 0.210400866
-[145] 0.093855295 0.142735752 0.761364232 0.464621321 0.061121298 0.049605515
-[151] 0.394449212 0.413216316 0.374454880 0.168265886 0.055593257 0.097151257
-[157] 0.780802798 0.227744822 0.065636001 0.870926086 0.510225526 0.405327388
-[163] 0.845888486 0.768194100 0.251842178 0.746537224 0.380903910 0.061307470
-[169] 0.290451789 0.578210814 0.667105491 0.436226140 0.501831663 0.314446545
-[175] 0.343212498 0.260100773 0.643573510 0.736279214 0.480576518 0.747377388
-[181] 0.084991510 0.625430254 0.563728676 0.065672797 0.586906305 0.383323904
-[187] 0.665138618 0.182143650 0.322690699 0.150906032 0.377309335 0.652937289
-[193] 0.167981587 0.764145834 0.112547545 0.094783549 0.792890037 0.121846529
-[199] 0.506278225 0.227685925 0.449045336 0.931289729 0.876824814 0.350126804
-[205] 0.010994635 0.786543245 0.709228011 0.596225473 0.378512582 0.664875538
-[211] 0.373815801 0.426252115 0.280636713 0.083968169 0.814257670 0.515317294
-[217] 0.328967379 0.099903254 0.377995332 0.473178827 0.031442176 0.447588371
-[223] 0.217327056 0.361663573 0.067611532 0.801212727 0.247607998 0.049270220
-[229] 0.576458572 0.324353916 0.136902467 0.001605048 0.257187329 0.474040408
-[235] 0.437713533 0.456113202 0.577646467 0.085121494 0.715134572 0.457893436
-[241] 0.498003300 0.838971972 0.503169431 0.045336633 0.555881808 0.022418980
-[247] 0.194331580 0.334462487 0.373599846 0.438035879 0.509068595 0.599445606
-[253] 0.468924572 0.387566042 0.655708457 0.735283578 0.007562104 0.489784093
-[259] 0.562107349 0.302752252 0.766853423 0.218580395 0.381560134 0.755457200
-[265] 0.955922687 0.272025101 0.251443038 0.057390884 0.289182961 0.155876041
-[271] 0.393765386 0.507388247 0.120671623 0.799010545 0.523637750 0.767031675
-[277] 0.010792082 0.619446682 0.047529687 0.786968380 0.413820169 0.318351521
-[283] 0.835610923 0.218595381 0.278001748 0.129935495 0.147382642 0.715055153
-[289] 0.544744934 0.786205589 0.344101052 0.302395620 0.488473797 0.066509090
-[295] 0.889908357 0.401278597 0.705956499 0.752430964 0.408975709 0.316904963
->
->
->
-> cleanEx()
-> nameEx("BiCopChiPlot")
-> ### * BiCopChiPlot
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopChiPlot
-> ### Title: Chi-plot for bivariate copula data
-> ### Aliases: BiCopChiPlot
->
-> ### ** Examples
->
-> ## Not run:
-> ##D # chi-plots for bivariate Gaussian copula data
-> ##D n = 500
-> ##D tau = 0.5
-> ##D
-> ##D # simulate copula data
-> ##D fam = 1
-> ##D theta = BiCopTau2Par(fam,tau)
-> ##D dat = BiCopSim(n,fam,theta)
-> ##D
-> ##D # create chi-plots
-> ##D dev.new(width=16,height=5)
-> ##D par(mfrow=c(1,3))
-> ##D BiCopChiPlot(dat[,1],dat[,2],xlim=c(-1,1),ylim=c(-1,1),
-> ##D main="General chi-plot")
-> ##D BiCopChiPlot(dat[,1],dat[,2],mode="lower",xlim=c(-1,1),
-> ##D ylim=c(-1,1),main="Lower chi-plot")
-> ##D BiCopChiPlot(dat[,1],dat[,2],mode="upper",xlim=c(-1,1),
-> ##D ylim=c(-1,1),main="Upper chi-plot")
-> ## End(Not run)
->
->
->
-> cleanEx()
-> nameEx("BiCopDeriv")
-> ### * BiCopDeriv
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopDeriv
-> ### Title: Derivatives of a bivariate copula density
-> ### Aliases: BiCopDeriv
->
-> ### ** Examples
->
-> # simulate from a bivariate t-copula
-> simdata = BiCopSim(300,2,-0.7,par2=4)
->
-> # derivative of the bivariate t-copula with respect to the first parameter
-> u1 = simdata[,1]
-> u2 = simdata[,2]
-> BiCopDeriv(u1,u2,2,-0.7,par2=4, deriv="par")
- [1] -1.118981963 1.240853696 -1.001064211 0.126342186 1.175103086
- [6] 1.125284074 -2.660900650 -2.472195467 1.023015475 -1.226835099
- [11] -10.758312703 -0.060805698 -1.275771122 -0.874257992 -5.229438212
- [16] -1.996785307 -0.024314703 -0.340297829 -1.124279340 -2.830928257
- [21] -0.644128690 -1.902583339 -0.198026426 -5.534636646 -0.399781564
- [26] 0.435400718 -0.424885018 1.472901008 -2.384264853 -2.524411180
- [31] -7.785438426 -1.363561693 -1.835639592 -0.722528253 -6.472291697
- [36] -0.716665042 -0.964105867 0.829886233 -4.540361690 1.351436336
- [41] -1.503828622 -1.074008721 -2.420782826 -0.457655215 1.239990732
- [46] 1.451559193 1.125103668 0.869236569 -1.879796413 -1.231228404
- [51] -2.424107089 1.829094173 -1.221019510 -2.559081446 -1.233727812
- [56] 1.872628051 -1.838289517 0.971594798 -0.096706898 -1.937842204
- [61] -15.237257119 0.172804942 -2.778179365 -0.368238987 -3.191034202
- [66] 1.081925107 -13.105195405 -1.253692526 -2.123195334 -8.688276102
- [71] -1.503515318 0.256531438 -2.674138975 -4.198369584 -5.259632795
- [76] -1.944954160 -1.969911957 0.006893071 1.207272916 0.571959168
- [81] -0.132434126 -0.754275953 -4.565334109 0.127084068 -2.903169549
- [86] 0.771316852 -4.357864429 0.489405734 -0.218840978 1.257765042
- [91] 0.720810321 -3.023537238 -5.465362366 0.055533560 -2.703654480
- [96] -2.809374993 -2.368171453 -2.796443300 -5.602431270 -1.693541846
-[101] 0.551536277 -1.064635971 -2.851261121 0.383686061 -2.937039105
-[106] -13.728663798 1.428865348 -4.156683579 1.475522589 -11.428897046
-[111] 1.042581628 -2.329817184 -3.371593709 1.422863380 0.041637025
-[116] 1.087794501 -2.304816570 -1.944080252 -1.899435223 -2.039960766
-[121] -2.645050409 -6.650555229 0.050104775 0.851039002 1.383789144
-[126] 1.465850488 -2.014890698 0.938868515 -2.392303876 1.122254766
-[131] -1.525108901 -2.071212850 -1.173020644 -10.741866594 -0.542828355
-[136] 1.515200988 0.630401901 0.694797858 0.931185696 -0.680191222
-[141] -7.933722404 -5.163276704 1.664827305 0.960737875 1.063045879
-[146] 1.342918851 -25.249465414 -1.393407553 1.532198588 -6.911077681
-[151] 0.376116964 -2.132593253 1.552339321 -3.505820153 -1.224401790
-[156] 1.227776973 0.353972461 -2.323316239 -0.313598837 -0.566267188
-[161] -2.330830899 1.274204534 1.293889346 0.860444774 -2.469578460
-[166] -37.208160647 -0.658022946 -0.468399340 0.542603267 1.471761927
-[171] -62.570186831 -2.007473905 -2.147550775 0.259715654 -1.822814641
-[176] 1.381625780 -3.213788742 -28.991603753 -1.229075644 -3.627148879
-[181] -0.101623278 -4.587179691 0.581309497 -9.140049046 -2.507966161
-[186] -2.212019166 -9.855455970 -1.589138378 -0.337898112 -2.553649116
-[191] -2.226506883 1.333901700 0.465082093 1.189110937 0.796068401
-[196] 1.443132908 -3.501076181 -0.501800492 -2.291129850 0.935368493
-[201] -1.064749466 1.467016724 1.082767823 -1.259347011 -35.328425259
-[206] -54.951284051 0.718174055 -2.464860924 -1.312713214 0.813159659
-[211] -2.210898561 -0.448716041 -1.620526551 -2.795506995 1.473304529
-[216] -5.057278714 -2.325488643 -6.612891371 1.047891665 1.184335554
-[221] 1.067936715 -1.893917726 -1.660850454 1.235448817 1.395848509
-[226] -8.352631627 0.661067847 -10.045949778 1.301830099 1.184362514
-[231] -2.365111887 -19.375622882 0.787353636 -1.284525224 -1.465763628
-[236] 1.487875678 -2.800629638 0.974747012 -3.893986259 -1.347791384
-[241] -1.715161311 1.344923130 -2.191660590 0.729214728 -4.251170941
-[246] -15.846996489 0.963319101 -2.045913223 -2.214253517 0.123732841
-[251] -1.130187683 -0.976720057 1.550396563 -0.114614937 -2.106725798
-[256] -3.543205014 -64.153096610 -1.674467836 -2.013424285 -2.506361843
-[261] 0.171864835 1.109748552 1.058702426 0.607026838 0.841762192
-[266] 1.600486017 -0.341279447 1.218882366 -1.075804146 -3.976274166
-[271] 1.462331510 -2.327053523 1.443964144 -70.883566817 -0.662165420
-[276] -1.690102935 -50.916636504 -3.285496238 1.214680711 1.243536650
-[281] -0.209042356 -1.987493543 0.412999360 -3.236351739 1.359980628
-[286] -1.527350949 1.444523027 -2.321816594 -2.549388281 0.552707478
-[291] 1.307238920 1.553288676 -2.277551572 -4.393119872 1.180012547
-[296] -1.006935254 -0.716277972 -0.594563456 -2.172846043 -1.984262894
->
->
->
-> cleanEx()
-> nameEx("BiCopDeriv2")
-> ### * BiCopDeriv2
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopDeriv2
-> ### Title: Second derivatives of a bivariate copula density
-> ### Aliases: BiCopDeriv2
->
-> ### ** Examples
->
-> # simulate from a bivariate t-copula
-> simdata = BiCopSim(300,2,-0.7,par2=4)
->
-> # second derivative of the bivariate t-copula with respect to the first parameter
-> u1 = simdata[,1]
-> u2 = simdata[,2]
-> BiCopDeriv2(u1,u2,2,-0.7,par2=4, deriv="par")
- [1] 2.4126542 -6.6825108 -6.3560370 -13.7227857 -9.1816929
- [6] -7.0908205 13.7290309 10.7473379 1.2577657 2.6630769
- [11] 45.1413216 -5.6405356 3.4557714 -38.1175487 23.8842516
- [16] 10.4429381 -3.4900896 -4.0350153 -5.2822373 14.4093322
- [21] -2.3705610 6.3865847 -2.3729617 -8.3403632 -1.7953292
- [26] -5.9126744 -0.8925703 -2.7198451 12.1687196 13.2898122
- [31] 34.8767633 5.5283219 6.8140897 0.5179240 12.8722339
- [36] -1.9694262 2.3857559 -7.1967954 19.9757800 -0.8634186
- [41] 5.8194993 3.3938077 7.1423000 -5.1528390 -6.5129152
- [46] -2.6078043 -6.5350832 -6.9423348 9.5969194 1.2028442
- [51] 12.2097919 -5.3415160 2.3088684 6.2549771 -6.3055972
- [56] -20.6839367 8.6985442 1.8900924 -7.2938766 9.6259884
- [61] -15.8136161 -4.4191251 13.3567417 -1.6699869 15.6181921
- [66] -8.2228638 49.6433734 -6.8637577 11.5149595 -10.1757433
- [71] 5.7891358 -5.1789980 13.2930910 17.9367741 11.1038423
- [76] 9.7799834 9.3298570 -3.7109533 -7.6361988 -5.8621277
- [81] -7.0977482 0.3699164 18.6443999 -12.3991863 14.2560643
- [86] -6.1678671 19.3905631 -7.4826160 -2.0968065 -6.4709585
- [91] -6.1599241 7.7973748 19.3468978 -4.3365165 -3.1372378
- [96] 14.3722621 -12.2920083 14.3064742 16.3602446 4.6174577
-[101] -5.8752261 2.9774240 11.4235543 -6.1721458 13.0711948
-[106] 24.5566247 -2.0566903 19.3946357 -3.4040873 46.5514372
-[111] -6.6975421 11.8752549 4.8264870 -1.9410271 -4.8470163
-[116] -6.5319545 11.8388787 8.7842751 8.5351932 10.8108849
-[121] 13.7383533 17.5443858 -3.4835638 -6.5227577 -7.3817498
-[126] -3.1497797 10.5288361 -7.0505394 10.7116425 -9.1255948
-[131] 6.4561544 6.1147706 -0.1415321 47.2160109 -2.1404643
-[136] -3.2675803 -5.8061782 -6.0783318 -6.6882969 0.2205718
-[141] 9.2056534 22.6513172 -5.4130890 -6.4797445 -8.6119575
-[146] -6.1626208 99.0525948 5.6320135 -4.5034060 -10.8423355
-[151] -8.2161180 11.7010068 -3.1354411 10.6979666 -7.4363695
-[156] -7.9862145 -7.0399481 10.7286487 -9.0056350 -19.8533850
-[161] 10.9014112 -0.7675042 -0.2967231 -7.3862615 9.1439497
-[166] 155.9358951 -1.0080337 -9.1237377 -5.7171014 -2.9464925
-[171] 269.9285276 10.3593989 8.7543815 -4.7214883 7.2427847
-[176] -6.7186224 15.3700517 124.6436795 -9.6978141 11.7143732
-[181] -7.6979536 20.5984504 -5.7257439 40.5312907 12.7734411
-[186] 12.0655708 42.4723521 4.4524000 -4.4733160 9.1906767
-[191] 12.1270553 -0.8568985 -5.9649734 -7.0021920 -23.9452815
-[196] -6.2800218 5.3513165 -3.9053336 10.6781184 -6.4459971
-[201] 0.4934674 -2.4424125 -13.7947217 2.6918629 -15.0437654
-[206] 179.5492436 -6.4095059 12.3367635 5.0010992 -6.3084576
-[211] 12.1575256 -3.7222823 6.9552673 -14.7678286 -5.2660209
-[216] -11.0043502 12.5483203 25.2705550 -12.1320323 -7.5170928
-[221] -16.7607915 9.6529562 5.9831467 -9.2588542 -1.5943981
-[226] 19.2252029 -5.8827620 41.3950758 -0.7504865 -8.7125535
-[231] 7.1471377 -182.3423219 -11.5964015 0.1386318 5.9833540
-[236] -6.7067508 14.3516973 -9.2066813 15.6816459 5.2243435
-[241] 1.1197675 -0.8486390 11.8796392 2.1755199 7.3798075
-[246] -19.7251148 -6.5430234 10.6690874 12.1767844 -16.2087793
-[251] 3.8184437 2.4339272 -5.1251428 -4.2674313 8.5722830
-[256] 12.3852346 276.5338339 3.0855253 9.9835689 12.7594328
-[261] -6.1429545 -6.5277846 -8.7959204 -6.8613633 3.2834040
-[266] -6.8909722 -1.5026474 0.2997642 3.2883771 18.6179306
-[271] -8.1021536 11.2468036 -5.3373429 195.6539596 0.6054940
-[276] 0.9947356 122.5599083 16.1741216 0.2079989 -7.1625500
-[281] -4.9097796 7.9203020 -10.0926920 15.9586637 -11.5061335
-[286] 1.7095188 -4.9261936 7.3923584 13.3974394 -7.5603040
-[291] -9.6068616 -8.6709300 12.2354089 11.2373337 -17.6110050
-[296] 2.1594059 -0.8513871 -2.9233828 12.0301834 10.0336656
->
->
->
-> cleanEx()
-> nameEx("BiCopEst")
-> ### * BiCopEst
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopEst
-> ### Title: Parameter estimation for bivariate copula data using inversion
-> ### of Kendall's tau or maximum likelihood estimation
-> ### Aliases: BiCopEst
->
-> ### ** Examples
->
-> ## Example 1: bivariate Gaussian copula
-> dat = BiCopSim(500,1,0.7)
-> u1 = dat[,1]
-> v1 = dat[,2]
->
-> # empirical Kendall's tau
-> tau1 = cor(u1,v1,method="kendall")
->
-> # inversion of empirical Kendall's tau
-> BiCopTau2Par(1,tau1)
-[1] 0.7045111
-> BiCopEst(u1,v1,family=1,method="itau")$par
-[1] 0.7045111
->
-> # maximum likelihood estimate for comparison
-> BiCopEst(u1,v1,family=1,method="mle")$par
-[1] 0.703239
->
->
-> ## Example 2: bivariate Clayton and survival Gumbel copulas
-> # simulate from a Clayton copula
-> dat = BiCopSim(500,3,2.5)
-> u2 = dat[,1]
-> v2 = dat[,2]
->
-> # empirical Kendall's tau
-> tau2 = cor(u2,v2,method="kendall")
->
-> # inversion of empirical Kendall's tau for the Clayton copula
-> BiCopTau2Par(3,tau2)
-[1] 2.480802
-> BiCopEst(u2,v2,family=3,method="itau",se=TRUE)
-$par
-[1] 2.480802
-
-$par2
-[1] 0
-
-$se
-[1] 0.2366735
-
-$se2
-[1] 0
-
->
-> # inversion of empirical Kendall's tau for the survival Gumbel copula
-> BiCopTau2Par(14,tau2)
-[1] 2.240401
-> BiCopEst(u2,v2,family=14,method="itau",se=TRUE)
-$par
-[1] 2.240401
-
-$par2
-[1] 0
-
-$se
-[1] 0.1183367
-
-$se2
-[1] 0
-
->
-> # maximum likelihood estimates for comparison
-> BiCopEst(u2,v2,family=3,method="mle",se=TRUE)
-$par
-[1] 2.370793
-
-$par2
-[1] 0
-
-$se
-[1] 0.1337379
-
-$se2
-[1] 0
-
-> BiCopEst(u2,v2,family=14,method="mle",se=TRUE)
-$par
-[1] 2.244569
-
-$par2
-[1] 0
-
-$se
-[1] 0.08094233
-
-$se2
-[1] 0
-
->
->
->
->
-> cleanEx()
-> nameEx("BiCopGofTest")
-> ### * BiCopGofTest
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopGofTest
-> ### Title: Goodness-of-fit test for bivariate copulas
-> ### Aliases: BiCopGofTest
->
-> ### ** Examples
->
-> # simulate from a bivariate Clayton copula
-> simdata = BiCopSim(300,3,2)
-> u1 = simdata[,1]
-> u2 = simdata[,2]
->
-> # perform White's goodness-of-fit test for the true copula
-> BiCopGofTest(u1,u2,family=3)
-$p.value
- [,1]
-[1,] 0.174567
-
-$statistic
- [,1]
-[1,] 1.84328
-
->
-> # perform Kendall's goodness-of-fit test for the Frank copula
-> BiCopGofTest(u1,u2,family=5)
-$p.value
- [,1]
-[1,] 0.499615
-
-$statistic
- [,1]
-[1,] 0.4557542
-
->
-> ## Not run:
-> ##D # perform Kendall's goodness-of-fit test for the true copula
-> ##D gof = BiCopGofTest(u1,u2,family=3,method="kendall")
-> ##D gof$p.value.CvM
-> ##D gof$p.value.KS
-> ##D
-> ##D # perform Kendall's goodness-of-fit test for the Frank copula
-> ##D gof = BiCopGofTest(u1,u2,family=5,method="kendall")
-> ##D gof$p.value.CvM
-> ##D gof$p.value.KS
-> ## End(Not run)
->
->
->
-> cleanEx()
-> nameEx("BiCopHfunc")
-> ### * BiCopHfunc
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopHfunc
-> ### Title: Conditional distribution function (h-function) of a bivariate
-> ### copula
-> ### Aliases: BiCopHfunc
->
-> ### ** Examples
->
-> # load data set
-> data(daxreturns)
->
-> # h-functions of the Gaussian copula
-> h1 = BiCopHfunc(daxreturns[,2],daxreturns[,1],1,0.5)
->
->
->
-> cleanEx()
-> nameEx("BiCopHfuncDeriv")
-> ### * BiCopHfuncDeriv
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: BiCopHfuncDeriv
-> ### Title: Derivatives of the h-function of a bivariate copula
-> ### Aliases: BiCopHfuncDeriv
->
-> ### ** Examples
->
-> # simulate from a bivariate t-copula
-> simdata = BiCopSim(300,2,-0.7,par2=4)
->
-> # derivative of the conditional bivariate t-copula
-> # with respect to the first parameter
-> u1 = simdata[,1]
-> u2 = simdata[,2]
-> BiCopHfuncDeriv(u1,u2,2,-0.7,par2=4, deriv="par")
- [1] 0.188249864 -0.529552481 -0.873071979 -0.088028377 0.764465081
- [6] 0.268742673 0.225414839 0.003745044 -0.250411287 -0.552223280
- [11] 0.542208002 0.733893277 0.171342121 0.040807295 0.271432180
- [16] -0.194478681 0.539020302 -0.179847959 0.857056553 0.165863800
- [21] -0.167799319 -0.078433467 -0.493630542 0.014446390 -0.259425789
- [26] -0.611885828 0.430828031 0.081037733 -0.034159823 0.180985090
- [31] 0.348575968 0.309101636 0.490238920 -0.491592260 -0.833857762
- [36] -0.683462885 0.269070347 -0.635325777 0.174910232 -0.232964155
- [41] -0.415391436 0.301526190 0.625251401 0.781396939 0.315011241
- [46] 0.272627317 -0.447754453 -0.281985372 0.171307540 -0.133608127
- [51] 0.291907921 -0.739724153 0.574547234 0.027070515 -0.078697407
- [56] -0.055992298 0.122430683 0.125977620 0.815021190 -0.275509931
- [61] 0.002381546 0.545961005 0.080450266 0.523905668 -0.172254706
- [66] 0.701785048 -0.172723789 -0.075921055 -0.050254540 -0.004517698
- [71] -0.168177303 0.276160659 0.085267141 -0.493157648 -0.803393801
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/vinecopula -r 65
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