copula.prob can be used to calculate the joint or conditional copula probabilities from a fitted simultaneous model with intervals obtained
via posterior simulation.
copula.prob(x, y1, y2, y3 = NULL, newdata, joint = TRUE, cond = 0,
intervals = FALSE, n.sim = 100, prob.lev = 0.05,
theta = FALSE, tau = FALSE, min.pr = 1e-323, max.pr = 1)
It returns several values including: estimated probabilities (p12), with lower and upper interval limits (CIpr)
if intervals = TRUE, and p1, p2 and p3 (the marginal probabilities).
A fitted gjrm object as
produced by the respective fitting function.
Value of response for first margin.
Value of response for second margin.
Value of response for third margin if a trivariate model is employed.
A data frame with one row, which must be provided.
If TRUE then the calculation is done using the fitted joint model. If FALSE then
the calculation is done from univariate fits.
There are three possible values: 0 (joint probabilities are delivered), 1 (conditional probabilities are delivered and conditioning is with the respect to the first margin), 2 (as before but conditioning is with the respect to the second margin).
If TRUE then intervals for the probabilities are also produced.
Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used for interval calculations.
Overall probability of the left and right tails of the probabilities' distributions used for interval calculations.
If TRUE the theta dependence parameter will be shown. This is especially useful for prediction purposes when theta is specified as
a function of covariate effects.
If TRUE the Kendall's tau will also be calculated and provided in output. Note that the calculation adopted here assumes continuous
margins. In all other cases, this may provide a rough indication of dependence under certain assumptions. Note that, for the F, PL and J0 (and the related
rotations), computing times may be longer than for the other cases. This is especially useful for prediction purposes when theta is specified as
a function of covariate effects, with an interest in analysing a more interpretable measure of dependence for certain copulae.
Allowed minimum and maximum for estimated probabities.
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
This function calculates joint or conditional copula probabilities from a fitted simultaneous model or a model assuming independence, with intervals obtained via posterior simulation.
GJRM-package, gjrm