| Title: | Smooth Test for Equality of Copulas and Clustering Multivariate |
|---|---|
| Description: | Implements approaches of non-parametric smooth test to compare simultaneously K(K>1) copulas and non-parametric clustering of multivariate populations with arbitrary sizes. See Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2112.05623> and Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2211.06338>. |
| Authors: | Yves Ismael Ngounou Bakam [aut, cre], Denys Pommeret [aut] |
| Maintainer: | Yves Ismael Ngounou Bakam <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.0 |
| Built: | 2025-03-15 03:56:26 UTC |
| Source: | https://github.com/cran/Kcop |
This function performs the data driven clustering procedure to cluster K multivariate populations of arbitrary sizes into N subgroups characterized by a common dependence structure where the number N of clusters is unknow and will be automatically chosen by our approach. The method is adapted to paired population and can be used with panel data. See the paper at the following arXiv weblink: https://arxiv.org/abs/2211.06338 for further information.
KcopClust(Kdata, dn = 3, paired = FALSE, alpha = 0.05)KcopClust(Kdata, dn = 3, paired = FALSE, alpha = 0.05)
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 |
alpha |
The significance level used in our decision rule. |
A list with three elements: the number of identified clusters; 2) the cluster affiliation; 3) the discrepancy matrix. the numbers in the clusters refer to the population indexes of the data list
Yves I. Ngounou Bakam and Denys Pommeret
## 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 clustering KcopClust(Kdata = Kdata)## 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 clustering KcopClust(Kdata = Kdata)
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)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)## 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)