rcop: Simulation from parametric bivariate copula families

In YafeiXu/CopulaModel: CopulaModel: Dependence Modeling with Copulas

Description

Simulation from parametric bivariate copula families (one-parameter and two-parameter)

Usage

#rcop(n,cpar)  
rcop(n,cpar,icheck=F) 

Arguments

n	sample size
cpar	copula parameter: could be scalar or vector depending on the copula family
icheck	if T, output average u, average v and (Spearman) correlation

Details

Choices are 'cop' in rcop

pla, frk, mtcj, joe, gum, gumr (reflected Gumbel), gal, hr, bb1, bb2, bb3, bb4, bb5, bb6, bb7, bb8, bb9, bb10 for bivariate families;

See help page for pcop for the abbreviations of the copula names.

breflasym, bpermasym, basymgum1, bMO1 for 'cop' in rcop for extreme cases of bivariate reflection or permutation asymmetric:

bivariate permutation asymmetric,

bivariate asymmetric Gummbel with one skew parameter,

bivariate Marshall-Olkin skew 1-parameter subfamily.

Value

nx2 matrix with values in (0,1)

See Also

pcop rbivcop2param

Examples

n=1000
set.seed(12345)
cpar=gum.tau2cpar(.45)
udat=rgum(n,cpar)
taucor(udat[,1],udat[,2])
cpar=depmeas2cpar(.45,"rhoS","gumbel")
udat=rgum(n,cpar)
cor(udat[,1],udat[,2],method="spearman")
cor(udat[,1],udat[,2])
# see help page for skewrefl for use of 
# rbreflasym, rbpermasym, rbsymgum1, rbMO1

YafeiXu/CopulaModel documentation built on May 9, 2019, 11:07 p.m.