PE: Partial effect from a binary bivariate model

In GJRM: Generalised Joint Regression Modelling

Partial effect from a binary bivariate model

Description

PE can be used to calculate the sample treatment effect from a a binary bivariate model, with corresponding interval obtained using posterior simulation.

Usage

PE(x1, idx, n.sim = 100, prob.lev = 0.05, 
   hd.plot = FALSE, 
   main = "Histogram and Kernel Density of Simulated Average Effects", 
   xlab = "Simulated Average Effects", ...)

Arguments

x1	A fitted gjrm object.
idx	This is useful to pick a particular individual and must be provided.
n.sim	Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used when delta = FALSE. It may be increased if more precision is required.
prob.lev	Overall probability of the left and right tails of the AT distribution used for interval calculations.
hd.plot	If TRUE then a plot of the histogram and kernel density estimate of the simulated average effects is produced.
main	Title for the plot.
xlab	Title for the x axis.
...	Other graphics parameters to pass on to plotting commands. These are used only when hd.plot = TRUE.

Details

PE measures the sample average effect from a binary bivariate model when a binary response (associated with a continuous outcome) takes values 0 and 1. Posterior simulation is used to obtain a confidence/credible interval.

Author(s)

Maintainer: Giampiero Marra [e-mail]

See Also

GJRM-package, gjrm

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