pbvt {weightedCL} | R Documentation |
BIVARIATE NORMAL AND STUDENT CDFs WITH VECTORIZED INPUTS
Description
Bivariate normal and Student cdfs with vectorized inputs
Usage
pbvt(z1,z2,param,icheck=FALSE)
Arguments
z1 |
scalar or vector of reals |
z2 |
scalar or vector of reals |
param |
vector of length 2, or matrix with 2 columns; vectors and number of rows of matrix cannot be different if larger than 1; for param, first column is rho, second column is df. |
icheck |
TRUE if checks are made for proper inputs, default of FALSE |
Value
cdf value(s)
References
Joe H (2014) CopulaModel: Dependence Modeling with Copulas. Software for book: Dependence Modeling with Copulas, Chapman & Hall/CRC, 2014.
Examples
cat("\n pbvt rho changing\n")
z1=.3; z2=.4; rho=seq(-.9,.9,.1); nu=2
param=cbind(rho,rep(nu,length(rho)))
out1=pbvt(z1,z2,param)
print(cbind(rho,out1))
cat("\n pbvt z1 changing\n")
z1=seq(-2,2,.4)
z2=.4; rho=.5; nu=2
out2=pbvt(z1,z2,c(rho,nu))
print(cbind(z1,out2))
[Package weightedCL version 0.5 Index]