cond.mv: Conditional Mean/Variance from a Copula Model

In GJRM: Generalised Joint Regression Modelling

Conditional Mean/Variance from a Copula Model

Description

Function cond.mv can be used to calculate conditional means/variances from a copula model, with corresponding interval obtained using posterior simulation.

Usage

cond.mv(x, eq, y1 = NULL, y2 = NULL, newdata, fun = "mean", n.sim = 100, 
        prob.lev = 0.05)

Arguments

x	A fitted cond.mv object as produced by the respective fitting function.
eq	Equation of interest. From this, conditioning is also deduced.
y1, y2	Values for y1 and y2. Depending on the fitted model, one of them may be required.
newdata	A data frame with one row, which must be provided.
fun	Either mean or variance.
n.sim	Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters.
prob.lev	Overall probability of the left and right tails of the simulated distribution used for interval calculations.

Details

cond.mv() calculates the conditional mean or variance of copula models. Posterior simulation is used to obtain a confidence/credible interval.

Value

res	It returns three values: lower confidence interval limit, estimated conditional mean or variance and upper interval limit.
prob.lev	Probability level used.
sim.mv	It returns a vector containing simulated values of the conditional mean or variance. This is used to calculate intervals.

Author(s)

Maintainer: Giampiero Marra [e-mail]

See Also

GJRM-package, gjrm

GJRM documentation built on Oct. 25, 2024, 5:07 p.m.