fit_jags: Fit Bayesian model using MCMC

In COVID-19-Mobility-Data-Network/mobility: Tools for Analyzing Human Mobility Data

Description

This is a general wrapper function for fitting a model to data using the JAGS MCMC algorithm. The function uses code adapted from the rjags package. Options include calculating the Deviance Information Criterion (DIC) and running sampling chains in parallel.

Usage

fit_jags(
  jags_data,
  jags_model,
  params,
  n_chain = 2,
  n_burn = 1000,
  n_samp = 1000,
  n_thin = 1,
  DIC = FALSE,
  parallel = FALSE
)

Arguments

jags_data	a list of data objects
jags_model	character string giving the model specification in JAGS/BUGS syntax
params	vector of parameters to monitor
n_chain	number of MCMC sampling chains
n_burn	number of iterations to discard before sampling of chains begins (burn in)
n_samp	number of iterations to sample each chain
n_thin	interval to thin samples
DIC	logical indicating whether or not to calculate the Deviance Information Criterion (DIC)
parallel	logical indicating whether or not to run MCMC chains in parallel or sequentially (default = FALSE). An even number of chains is recommended when running in parallel.

Details

If JAGS library not already installed, install here: https://sourceforge.net/projects/mcmc-jags/files/JAGS/3.x/

Value

an mcmc.list object

Author(s)

John Giles

See Also

Other model: check(), compare(), fit_prob_travel(), mobility(), predict(), residuals(), summary()

Examples

N <- 100
lambda <- 10
y <- rpois(n=N, lambda=lambda)

jags_data <- list(N=N, y=y)

jags_model <- "
model {

  for(i in 1:N){

    y[i] ~ dpois(lambda)

  }

  lambda ~ dgamma(1, 0.01)

}"

params <- c('lambda')

mod <- fit_jags(jags_data=jags_data,
                jags_model=jags_model,
                params=params)

COVID-19-Mobility-Data-Network/mobility documentation built on Nov. 22, 2021, 12:17 a.m.