Average treatment effect from a two-part model

Share:

Description

AT2 can be used to calculate the sample average treatment effect from a two-part model, with corresponding interval obtained using posterior simulation.

Usage

1
2
3
4
AT2(x1, x2, index1, index2, 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 SemiParBIVProbit object as produced by SemiParBIVProbit().

x2

A fitted SemiParBIVProbit object as produced by SemiParBIVProbit().

index1

This is useful to pick a particular individual.

index2

As above.

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. This can only be produced when delta = FALSE.

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

AT measures the sample average effect from a two-part 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.

WARNINGS

This function is not suitable for SemiParBIVProbit().

Author(s)

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

See Also

SemiParBIVProbit-package, SemiParBIVProbit, summary.SemiParBIVProbit

Examples

1
## see examples for SemiParBIVProbit

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.