poimcar_cpp: POIMCAR

In DouglasMesquita/TGMRF: Transformed Gaussian Markov Random Fields

POIMCAR

Description

Multivariate Poisson regression with CAR covariance structure

Usage

poimcar_cpp(nsim, burnin, thin, eps, mu, beta, nu, rho_s, rho_t, rho_st, X,
  y, E, Ws, Wt, N, P, mean_beta, tau_beta, eta_nu, psi_nu, fix_rho_s,
  fix_rho_t, fix_rho_st, range_rho_s, range_rho_t, range_rho_st, type,
  var_beta_met, var_eps_met, var_log_mu_met, var_rho_met, var_log_nu_met,
  verbose, c_beta, c_eps, c_mu, c_nu, c_rho, conj_beta)

Arguments

nsim	MCMC size
X	Covariate matrix
y	Response
E	Offset
N	Dimension of the observations
P	Dimension of the covariates
mean_beta	Mean a priori to beta vector
tau_beta	Variance a priori to beta vector
eta_nu	Shape a priori to nu
psi_nu	Rate a priori to nu
type	TGMRF type (1 to 6)
var_beta_met	Variance of beta proposal
var_eps_met	Variance of eps proposal
var_rho_met	Variance of rho proposal
var_log_nu_met	Variance of log(nu) proposal
M	Number of neighbors in each area
W	Matrix with the neighborhood structure
rangeRho	Range to sample rho by using ARMS
method	ARMS (0) or Metropolis (1)
ninit	Number of initial points in ARMS
maxpoint	Maximum number of evaluation in each ARMS iteration
tau	Vector of tau parameters to construct Q

DouglasMesquita/TGMRF documentation built on May 28, 2022, 8:34 p.m.