Given a single joint probability denoted as \(t\) for a copula \(\mathbf{C}(u,v)\) numerically solve for bivariate marginal probabilities \(U\) and \(V\) such that they are also equal to each other (\(u = v = w\)). For the case of a joint and probability, the primary diagonal of the copula (Nelsen, 2006, pp. 12 and 16) is solved for by a simple dispatch to the diagCOPatf
function instead. Symbolically the solution is
$$\mathrm{Pr}[U \le v,\ V \le v] = t = \mathbf{C}(w,w)\mbox{.}$$
For the case of a joint or probability, the dual of a copula (function) or \(\tilde{\mathbf{C}}(u,v)\) from a copula (Nelsen, 2006, pp. 33--34; duCOP
) is used where symbolicaly the solution is
$$\mathrm{Pr}[U \le v \mathrm{\ or\ } V \le v] = t = \tilde{\mathbf{C}}(u,v) = u + v - \mathbf{C}(u,v)\mbox{,}$$
or
$$\mathrm{Pr}[U \le v \mathrm{\ or\ } V \le v] = t = 2w - \mathbf{C}(w,w)\mbox{.}$$
The function for type="or"
tests \(\tilde{\mathbf{C}}(0,0)\) and if it returns NA
or NaN
then the lower limit for the rooting is treated as .Machine$double.eps
instead of 0 (zero).