marg.mv: Marginal Mean/Variance

In GJRM: Generalised Joint Regression Modelling

Marginal Mean/Variance

Description

Function marg.mv can be used to calculate marginal means/variances, with corresponding interval obtained using posterior simulation.

Usage

marg.mv(x, eq, newdata, fun = "mean", n.sim = 100, prob.lev = 0.05, bin.model = NULL)

Arguments

x	A fitted marg.mv object as produced by the respective fitting function.
eq	Number of equation of interest.
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.
bin.model	If a two part or hurdle model is used then this is the object of a binary regression model fitted using gam() from mgcv.

Details

marg.mv() calculates the marginal mean or variance. Posterior simulation is used to obtain a confidence/credible interval.

Value

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