bayescmp: Fitting COM-Poisson (mean parametrization) Model Based on...

In jreduardo/bayescm: Bayesian Inference for Flexible Count Models

Description

Build matrices and organize data to use jags.model andcoda.samples for analysis of Bayesian models using Markov Chain Monte Carlo (MCMC).

Usage

bayescmp(formula, data, sumto, .control_model = control_model(),
  .control_samples = control_samples())

Arguments

formula	A formula to define fixed effects.
data	The data frame.
sumto	Upper bound to infinite sum of the normalization constant
.control_model	See control_model.
.control_samples	See control_samples.

Author(s)

Eduardo Jr

Examples

# Simulate data
beta <- c(3, -1)
X <- cbind(1, runif(50))
y <- rpois(50, lambda = exp(X %*% beta))

# Sampling and summarise posterior
model <- bayescmp(y ~ X - 1, sumto = 100L)
vapply(model$samples, function(x) apply(x, 2L, mean), double(3))

jreduardo/bayescm documentation built on May 17, 2019, 7:29 p.m.