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CDVineVuongTest(data, Model1.order=1:dim(data)[2], Model2.order=1:dim(data)[2], Model1.family, Model2.family, Model1.par, Model2.par, Model1.par2=rep(0,dim(data)[2]*(dim(data)[2]-1)/2), Model2.par2=rep(0,dim(data)[2]*(dim(data)[2]-1)/2), Model1.type, Model2.type)
Model1.order
and Model2.order = 1:dim(data)[2]
, i.e., standard order).0
= independence copula
1
= Gaussian copula
2
= Student t copula (t-copula)
3
= Clayton copula
4
= Gumbel copula
5
= Frank copula
6
= Joe copula
7
= BB1 copula
8
= BB6 copula
9
= BB7 copula
10
= BB8 copula
13
= rotated Clayton copula (180 degrees; ``survival Clayton'')
14
= rotated Gumbel copula (180 degrees; ``survival Gumbel'')
16
= rotated Joe copula (180 degrees; ``survival Joe'')
17
= rotated BB1 copula (180 degrees; ``survival BB1'')
18
= rotated BB6 copula (180 degrees; ``survival BB6'')
19
= rotated BB7 copula (180 degrees; ``survival BB7'')
20
= rotated BB8 copula (180 degrees; ``survival BB8'')
23
= rotated Clayton copula (90 degrees)
24
= rotated Gumbel copula (90 degrees)
26
= rotated Joe copula (90 degrees)
27
= rotated BB1 copula (90 degrees)
28
= rotated BB6 copula (90 degrees)
29
= rotated BB7 copula (90 degrees)
30
= rotated BB8 copula (90 degrees)
33
= rotated Clayton copula (270 degrees)
34
= rotated Gumbel copula (270 degrees)
36
= rotated Joe copula (270 degrees)
37
= rotated BB1 copula (270 degrees)
38
= rotated BB6 copula (270 degrees)
39
= rotated BB7 copula (270 degrees)
40
= rotated BB8 copula (270 degrees)
Model1.family
/Model2.family
,
these arguments do not need to be specified
(default: Model1.par2
and Model2.par2 = rep(0,dim(data)[2]*(dim(data)[2]-1)/2)
).1
or "CVine"
= C-vine
2
or "DVine"
= D-vineLike AIC and BIC, the Vuong test statistic may be corrected for the number of parameters used in the models. There are two possible corrections; the Akaike and the Schwarz corrections, which correspond to the penalty terms in the AIC and the BIC, respectively.
CDVineClarkeTest
, CDVineAIC
, CDVineBIC
## Not run:
# # load data set
# data(worldindices)
# d = dim(worldindices)[2]
#
# # select the C-vine families and parameters
# cvine = CDVineCopSelect(worldindices,c(1:6),type="CVine")
#
# # select the D-vine families and parameters
# dvine = CDVineCopSelect(worldindices,c(1:6),type="DVine")
#
# # compare the two models based on the data
# vuong = CDVineVuongTest(worldindices,1:d,1:d,cvine$family,dvine$family,
# cvine$par,dvine$par,cvine$par2,dvine$par2,
# Model1.type=1,Model2.type=2)
# vuong$statistic
# vuong$statistic.Schwarz
# vuong$p.value
# vuong$p.value.Schwarz
# ## End(Not run)
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