chaingibbs: Generate Gibbs samplers for counterfactual collective...

In youjin1207/netchain: Inferring Causal Effects on Collective Outcomes under Interference

Description

This function generates the outcomes using Gibbs sampling under the given treatment assignment and edge information.

Usage

chaingibbs(pars, n.obs, treatment, covariates, initprob = 0.5,
  yvalues = c(0, 1), Neighborind, Neighborpar, n.burn)

Arguments

pars	a set of parameters
n.obs	the number of Gibbs samples.
treatment	a set of given treatment assignment of length m.
covariates	given confounder(s): NULL: no confounder. a vector of length m: under unique confounder. a [q x m] matrix: a set of q different confounders.
initprob	an initial probability generating outcomes. Defaults to initprob = 0.5
yvalues	distinct binary values for outcomes. Defaults to (0,1).
Neighborind	a list of matrix specifying edge information of which first column illustrates a type of variables (1:outcome, 2:treatment, 3~:confounders) and of which second column presents the index of those variable.
Neighborpar	index for parameters in the order of Neighborind.
n.burn	the number of burn-in sample in Gibbs sampling (≥ n.obs).

Value

a [n.obs x m] matrix each row of which consists of outcomes.

youjin1207/netchain documentation built on May 7, 2019, 9:32 a.m.