pred.mvt: Function to predict mean and variance of marginal...

In GJRM: Generalised Joint Regression Modelling

Function to predict mean and variance of marginal distributions, as well as Kendall's tau

Description

It takes a fitted gjrm object produced by gjrm() and produces predictions and respective intervals.

Usage

pred.mvt(x, eq, fun = "mean", n.sim = 100, prob.lev = 0.05, smooth.no = NULL, ...)

Arguments

x	A fitted gjrm object.
eq	The equation number.
fun	Either mean, variance or tau.
n.sim	The number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used to calculate intervals. It may be increased if more precision is required.
prob.lev	Probability of the left and right tails of the posterior distribution used for interval calculations.
smooth.no	Smooth number if the interest is in a particular smooth and not the additive predictor(s).
...	Other parameters.

Author(s)

Maintainer: Giampiero Marra [e-mail]

See Also

gjrm

GJRM documentation built on July 9, 2023, 7:15 p.m.