KcopTest | R Documentation |
This functions performs the nonparametric smooth test to compare simultaneously K(K>1) copulas. See 'Details' below for further information.
KcopTest(Kdata, dn = 3, paired = FALSE)
Kdata |
A list of the K dataframe or matrix |
dn |
Number of copulas coefficients considered |
paired |
A logical indicating whether to consider the datas as paired |
Recall that we have K multivariate populations of arbitrary sizes, possibly paired
with unknow associated copulas C_1,...,C_K
respectively. KcopTest
performs the
following hypothesis H0: C_1=C_2=...=C_K against H1: C_l differs from C_m
(l different from m and l,m in 1:K). The test is based on copulas
cross-moments founded on Legendre polynomials that he called copulas coefficients.
See the paper at the following HAL weblink: https://hal.archives-ouvertes.fr/hal-03475324v2
A list with three elements: the p-value of the test, the value of the test statistic and the selected rank of copulas coefficients (number of terms involved in the test statistic)
Yves Ismael Ngounou Bakam
## simulation of 5 three-dimensional populations of different sizes Packages <- c("copula","gtools","dplyr", "orthopolynom", "stats") lapply(Packages, library, character.only = TRUE) # if necessary set.seed(2022) dat1<-rCopula(50, copula = gumbelCopula(param=6,dim = 2)) dat2<-rCopula(60, copula = claytonCopula(param=0.4,dim = 2)) dat3<-rCopula(55, copula = claytonCopula(param=0.4,dim = 2)) ## Form a list of data Kdata<-list(data1=dat1,data2=dat2,data3=dat3) ## Applying the test KcopTest(Kdata = Kdata)
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