| dcopula.t {QRMlib} | R Documentation |
evaluates density of t copula
dcopula.t(u, nu, P, logvalue=FALSE)
u |
matrix of dimension n times d, where d is the dimension of the copula and n is the number of vector values at which to evaluate density |
nu |
degrees of freedom of t copula |
P |
correlation matrix of t copula |
logvalue |
whether or not log density values should be returned (useful for ML) |
see pages 197 and 235 of QRM
vector of density values of length n
dmt,
dcopula.clayton,
dcopula.gumbel,
dcopula.gauss
## Not run:
ll <- c(0.01,0.99);
#create perspective plot for bivariate density:
BiDensPlot(func=dcopula.t,xpts=ll,ypts=ll,nu=4,P=equicorr(2,0.5));
#define an internal function:
normal.metat <- function(x,nu,P)
{
exp(dcopula.t(apply(x,2,pnorm),nu,P,logvalue=TRUE) +
apply(log(apply(x,2,dnorm)),1,sum));
}
#use function to create perspective plot for bivariate density:
BiDensPlot(normal.metat,xpts=ll,ypts=ll,npts=80,nu=4,P=equicorr(2,0.71));
## End(Not run)