auxVarCpp: Run Gibbs sampler for auxiliary covariate values using Rcpp

In isabelfulcher/autognet: Estimation of causal network effects

Description

Given the specific inputs, determine auxiliary covariate values using a Gibbs sampling procedure.

Usage

auxVarCpp(
  tau,
  rho,
  nu,
  N,
  R,
  J,
  rho_mat,
  adjacency,
  cov_i,
  weights,
  group_lengths,
  group_functions,
  additional_nu
)

Arguments

tau	A numeric vector for the intercept terms in the covariate model
rho	A numeric vector for the correlation terms in the covariate model
nu	A numberic matrix for the neighbor terms in the covariate model
N	An integer indicating the size of the interconnected network
R	An integer indicating the number of iterations for the Gibbs
J	An integer for the number of covariates
rho_mat	A numeric matrix for rho terms
adjacency	A binary matrix indicating connected units
cov_i	A numeric matrix for observed covariate values (starting values for chain)
weights	A numeric vector indicating the number of neighbors for each node
group_lengths	An integer vector indicating the number of categories for each variable
group_functions	An integer vector indicating the type of variable
additional_nu	An integer (0/1) specifying whether neighbor cross terms will be evaluated (i.e. non zero)

Value

A numeric matrix for auxiliary covariate values between [0,1]

isabelfulcher/autognet documentation built on Aug. 23, 2020, 9:42 a.m.