tests/additonalExampleRuns.R

In tnagler/VineCopula: Statistical Inference of Vine Copulas

## switch for testing the following (time consuming) examples
docheck <- FALSE

if(docheck){

### tests from excluded examples
library(VineCopula)
data(daxreturns)

## Not run:
# simulate from a bivariate Clayton copula
set.seed(123)
simdata <- BiCopSim(300, 3, 2)
u1 <- simdata[,1]
u2 <- simdata[,2]

# perform Kendall's goodness-of-fit test for the true copula
gof <- BiCopGofTest(u1, u2, family = 3, method = "kendall")
gof$p.value.CvM
gof$p.value.KS

# perform Kendall's goodness-of-fit test for the Frank copula
gof <- BiCopGofTest(u1, u2, family = 5, method = "kendall")
gof$p.value.CvM
gof$p.value.KS
# End(Not run)


## Not run:
# simulate from a t-copula
set.seed(123)
dat <- BiCopSim(500, 2, 0.7, 5)

# apply the test for families 1-10
vcgof <- BiCopVuongClarke(dat[,1], dat[,2], familyset = c(1:10))

# display the Vuong test scores
vcgof[1,]
# End(Not run)

## Not run:
# select the R-vine structure, families and parameters
RVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6))
RVM$Matrix
RVM$par
RVM$par2

# select the C-vine structure, families and parameters
CVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6), type = "CVine")
CVM$Matrix
CVM$par
CVM$par2

# compare the two models based on the data
clarke <- RVineClarkeTest(daxreturns[,1:5], RVM, CVM)
clarke$statistic
clarke$statistic.Schwarz
clarke$p.value
clarke$p.value.Schwarz
# End(Not run)

## Not run:
# White test with asymptotic p-value
RVineGofTest(daxreturns[,1:5], RVM, B = 0)

# ECP2 test with Cramer-von-Mises test statistic and a bootstrap with 200 replications
# for the calculation of the p-value
RVineGofTest(daxreturns[,1:5], RVM, method = "ECP2", statistic = "CvM", B = 200)
# End(Not run)

## Not run:
# define 5-dimensional R-vine tree structure matrix
Matrix <- c(5, 2, 3, 1, 4,
            0, 2, 3, 4, 1,
            0, 0, 3, 4, 1,
            0, 0, 0, 4, 1,
            0, 0, 0, 0, 1)
Matrix <- matrix(Matrix, 5, 5)

# define R-vine pair-copula family matrix
family <- c(0, 1, 3, 4, 4,
            0, 0, 3, 4, 1,
            0, 0, 0, 4, 1,
            0, 0, 0, 0, 3,
            0, 0, 0, 0, 0)
family <- matrix(family, 5, 5)

# define R-vine pair-copula parameter matrix
par <- c(0, 0.2, 0.9, 1.5, 3.9,
         0, 0, 1.1, 1.6, 0.9,
         0, 0, 0, 1.9, 0.5,
         0, 0, 0, 0, 4.8,
         0, 0, 0, 0, 0)
par <- matrix(par, 5, 5)

# define second R-vine pair-copula parameter matrix
par2 <- matrix(0, 5, 5)

# define RVineMatrix object
RVM <- RVineMatrix(Matrix = Matrix, family = family, par = par, par2 = par2,
                   names=c("V1", "V2", "V3", "V4", "V5"))

# simulate a sample of size 300 from the R-vine copula model
set.seed(123)
simdata <- RVineSim(300, RVM)

# compute the MLE
mle <- RVineMLE(simdata, RVM, grad = TRUE)
mle$RVM
# End(Not run)

## Not run:
# PIT data
pit <- RVinePIT(daxreturns[,1:5], RVM)

par(mfrow = c(1,2))
plot(daxreturns[,1], daxreturns[,2])  # correlated data
plot(pit[,1], pit[,2])	# i.i.d. data

cor(daxreturns[,1:5], method = "kendall")
cor(pit, method = "kendall")
# End(Not run)

##TODO shorten this test, takes too long
# # Not run:
# RVM <- RVineStructureSelect(daxreturns, c(1:6), progress=TRUE)
# # End(Not run)
#
# # specify a C-vine copula model with only Clayton, Gumbel and Frank copulas
# # Not run:
# CVM <- RVineStructureSelect(daxreturns, c(3,4,5), "CVine")
# # End(Not run)
# # determine the order of the nodes in a D-vine using the package TSP
# # Not run:
# library(TSP)
# d <- dim(daxreturns)[2]
# M <- 1 - abs(TauMatrix(daxreturns))
# hamilton <- insert_dummy(TSP(M), label = "cut")
# sol <- solve_TSP(hamilton,method = "repetitive_nn")
# order <- cut_tour(sol, "cut")
# DVM <- D2RVine(order, family = rep(0,d*(d-1)/2), par = rep(0,d*(d-1)/2))
# RVineCopSelect(daxreturns, c(1:6), DVM$Matrix)
# End(Not run)

## Not run:
RVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6))
CVM <- RVineStructureSelect(daxreturns[,1:5], c(1:6), type = "CVine")

# compare the two models based on the data
vuong <- RVineVuongTest(daxreturns[,1:5], RVM, CVM)
vuong$statistic
vuong$statistic.Schwarz
vuong$p.value
vuong$p.value.Schwarz
# End(Not run)

}