Gauss_MCMC: MCMC Algorithm for conditional Gaussian likelihood

In drkowal/rSTAR: MCMC and EM algorithms for Simultaneous Transformation and Rounding (STAR) Models

MCMC Algorithm for conditional Gaussian likelihood

Description

Run the MCMC algorithm for a conditional Gaussian likelihood given (i) a function to initialize model parameters and (ii) a function to sample (i.e., update) model parameters. This is similar to the STAR framework, but without the transformation and rounding.

Usage

Gauss_MCMC(
  y,
  sample_params,
  init_params,
  nsave = 5000,
  nburn = 5000,
  nskip = 2,
  verbose = TRUE
)

Arguments

y	n x 1 vector of observations
sample_params	a function that inputs data y and a named list params containing mu n x 1 vector of conditional means (fitted values) sigma the conditional standard deviation coefficients a named list of parameters that determine mu and outputs an updated list params of samples from the full conditional posterior distribution of coefficients and sigma (and updates mu)
init_params	an initializing function that inputs data y and initializes the named list params of mu, sigma, and coefficients
nsave	number of MCMC iterations to save
nburn	number of MCMC iterations to discard
nskip	number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
verbose	logical; if TRUE, print time remaining

Value

a list with the following elements:

• coefficients the posterior mean of the coefficients

• fitted.values the posterior mean of the conditional expectation of the data y

• post.coefficients nsave posterior draws of the coefficients

• post.fitted.values nsave posterior draws of the conditional mean of y

• post.pred nsave draws from the posterior predictive distribution of y

• post.sigma nsave draws from the posterior distribution of sigma

• post.log.like.point nsave draws of the log-likelihood for each of the n observations

• logLik the log-likelihood evaluated at the posterior means

• WAIC Widely-Applicable/Watanabe-Akaike Information Criterion

• p_waic Effective number of parameters based on WAIC

Examples

# Fixme

drkowal/rSTAR documentation built on July 5, 2023, 2:18 p.m.