mcmc_cmp: MCMC Algorithm for Conway-Maxwell-Poisson Regression Model...

In MultRegCMP: Bayesian Multivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data

MCMC Algorithm for Conway-Maxwell-Poisson Regression Model for Multivariate Correlated Count Data

Description

MCMC Algorithm to estimate the parameters in the regression model for multivariate correlated count data

Usage

mcmc_cmp(
  y,
  X,
  S = 10000,
  nburn = 5000,
  initial_beta,
  initial_gamma,
  initial_b,
  prior_mean_beta,
  prior_var_beta,
  prior_mean_gamma,
  prior_var_gamma,
  v_0,
  R_0,
  intercept = FALSE,
  scale_b,
  scale_beta,
  scale_gamma,
  scale_cov_b,
  scale_cov_beta,
  scale_cov_gamma,
  inc_burn = FALSE,
  re_chain = TRUE,
  way = 2,
  random_seed,
  ...
)

Arguments

y	Matrix of observations
X	Covariates list, each element is the design matrix for each column of y
S	Number of MCMC samples to be drawn
nburn	Number of MCMC samples to burn-in
initial_beta	List with initial value of beta for each response
initial_gamma	List with initial value of gamma for each response
initial_b	Initital value of b.
prior_mean_beta	Prior mean for beta. (Default zero vector)
prior_var_beta	Prior covariance matrix for beta (Default I)
prior_mean_gamma	Prior mean for beta. (Default zero vector)
prior_var_gamma	Prior covariance matrix for gamma (Default I)
v_0	Prior degrees of freedom of random effects
R_0	Prior covariance matrix of random effects
intercept	Logical value indicating whether include the intercept
scale_b	Covariance matrix for RW proposals of the random effects (Default I)
scale_beta	List with initial values for the scale matrices of beta (Default I)
scale_gamma	List with initial values for the scale matrices of gamma (Default I)
scale_cov_b	Scale parameter for the RW of random effects. (Default 2.4/sqrt(2))
scale_cov_beta	Scale parameter for the covariance of the proposals.
scale_cov_gamma	Scale parameter for the covariance of the proposals.
inc_burn	logical: include burned samples in the return
re_chain	logical: If the posterior samples for the r.e are include. False return just the mean
way	How to calculate the MCMC updates, based on Chib (2001)
random_seed	Random seed
...	Additional parameters of the MCMC algorithm

Value

A list:

posterior_b	List with posterior values of the random effects
estimation_beta	Estimation of beta parameters
posterior_beta	List with posterior values of beta
estimation_gamma	Estimation of gamma parameters
posterior_gamma	List with posterior values of gamma
posterior_D	Values of covariance matrix D
fitted_mu	Posterior of location parameters for each response
fitted_nu	Posterior of shape parameters for ecah response
accept_rate_b	Acceptance rate of Random Effects
accept_rate_beta	Acceptance rate of beta
accept_rate_gamma	Acceptance rate of gamma
scale_beta	Estimated Scale matrix for beta parameters
scale_gamma	Estimated Scale matrix for gamma parameters
X	List of covariates used
y	Matrix of observed counts

Examples

  n = 50; J = 2
  X = list(matrix(rnorm(3*n), ncol = 3), matrix(rnorm(3*n), ncol = 3))
  beta <- list(c(1,0.1, 1), c(0, 0.5, -0.5))
  mu <- exp(prod_list(X, beta))
  y = matrix(rpois(n = length(mu), lambda = mu), nrow = n)
  fit <- mcmc_cmp(y, X, S = 10000, nburn = 1000, scale_cov_b = 0.8,
  scale_cov_beta = 0.04, scale_cov_gamma = 0.06)

MultRegCMP documentation built on June 22, 2024, 9:47 a.m.