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The Raftery copula (Nelsen, 2006, p. 172) is
where P
), and as M
). The parameter rhoCOP
) by tauCOP
) by COP
abstraction layer to acquire negative or inverse dependency (countermonotonicity) (see the Examples).
RFcop(u, v, para=NULL, rho=NULL, tau=NULL, fit=c("rho", "tau"), ...)
Value(s) for the copula are returned. Otherwise if either rho
or tau
is given, then the list
having
The parameter
Spearman Rho if the rho
is given; and
Kendall Tau if the tau
is given but also if both rho
and tau
are NULL
as mentioned next.
and if para=NULL
and rho
and tau=NULL
, then the values within u
and v
are used to compute Kendall Tau and then compute the parameter, and these are returned in the aforementioned list.
Nonexceedance probability
Nonexceedance probability
A vector (single element) of parameters---the
Optional Spearman Rho from which the parameter will be estimated and presence of rho
trumps tau
;
Optional Kendall Tau from which the parameter will be estimated;
If para
, rho
, and tau
are all NULL
, then the u
and v
represent the sample. The measure of association by the fit
declaration will be computed and the parameter estimated subsequently. The fit
has no other utility than to trigger which measure of association is computed internally by the cor
function in R; and
Additional arguments to pass.
W.H. Asquith
Nelsen, R.B., 2006, An introduction to copulas: New York, Springer, 269 p.
M
, P