pred.gp: Function to predict quantiles from GP and DGP distributions

In GJRM: Generalised Joint Regression Modelling

Function to predict quantiles from GP and DGP distributions

Description

It takes a fitted gamlss object produced by gamlss() and produces the desired quntities and respective intervals.

Usage

pred.gp(x, p = 0.5, newdata, n.sim = 100, prob.lev = 0.05)

Arguments

x	A fitted gamlss object.
p	Value of p.
newdata	A data frame or list containing the values of the model covariates at which predictions are required. If not provided then predictions corresponding to the original data are returned. When newdata is provided, it should contain all the variables needed for prediction.
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.

Author(s)

Maintainer: Giampiero Marra [e-mail]

See Also

gamlss

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