jc.probs: Joint or conditional probabilities from a fitted joint model

In GJRM: Generalised Joint Regression Modelling

Joint or conditional probabilities from a fitted joint model

Description

jc.probs can be used to calculate the joint or conditional probabilities from a fitted joint model with intervals obtained using posterior simulation.

Usage

jc.probs(x, y1, y2, y3 = NULL, newdata, type = "joint", cond = 0,
         intervals = FALSE, n.sim = 100, prob.lev = 0.05, min.pr = 1e-323, max.pr = 1, 
         cumul = "no")

Arguments

x	A fitted gjrm object as produced by the respective fitting function.
y1	Value of response for first margin.
y2	Value of response for second margin.
y3	Value of response for third margin if a trivariate model is employed.
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.
type	This argument can take two: "joint" (the probabilities are calculated from the fitted joint model) and "independence" (the calculation is done from univariate fits).
cond	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).
intervals	If TRUE then intervals for the probabilities are also produced.
n.sim	Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used for interval calculations.
prob.lev	Overall probability of the left and right tails of the probabilities' distributions used for interval calculations.
min.pr, max.pr	Allowed minimum and maximum for estimated probabities.
cumul	Only used for discrete and continuous margins' case.

Details

This function calculates joint or conditional probabilities from a fitted joint model or a model assuming independence, with intervals obtained using posterior simulation.

Value

res	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).

Author(s)

Maintainer: Giampiero Marra [e-mail]

See Also

GJRM-package, gjrm

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