PE: Partial effect from a binary bivariate model
In KironmoyDas/KD-STAT0035-GMupdate: Generalised Joint Regression Modelling

Description Usage Arguments Details Author(s) See Also

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

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", ...)

`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`.

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.

Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk

GJRM-package, gjrm

KironmoyDas/KD-STAT0035-GMupdate documentation built on Feb. 15, 2021, 12:17 a.m.