Bmchoice: Model choice criteria calculation for univariate normal model...

In sujit-sahu/bmstdr: Bayesian Modeling of Spatio-Temporal Data with R

Model choice criteria calculation for univariate normal model for both known and unknown sigma^2

Description

Model choice criteria calculation for univariate normal model for both known and unknown sigma^2

Usage

Bmchoice(
  case = "Exact.sigma2.known",
  y = ydata,
  mu0 = mean(y),
  sigma2 = 22,
  kprior = 1,
  prior.M = 1,
  prior.sigma2 = c(2, 1),
  N = 10000,
  rseed = 44
)

Arguments

case	One of the three cases: "Exact.sigma2.known": Use exact theoretical calculation. "MC.sigma2.known": Use Monte Carlo methods for drawing samples from the posterior assuming known sigma2. "MC.sigma2.unknown": Use the Gibbs sampler to generate samples from the joint posterior distribution of theta and sigma^2.
y	A vector of data values. Default is 28 ydata values from the package bmstdr
mu0	The value of the prior mean if kprior=0. Default is the data mean.
sigma2	Value of the known data variance; defaults to sample variance of the data. This is ignored in the third case when sigma2 is assumed to be unknown.
kprior	A scalar providing how many data standard deviation the prior mean is from the data mean. Default value is 0.
prior.M	Prior sample size, defaults to 10^(-4).
prior.sigma2	Shape and scale parameter value for the gamma prior on 1/sigma^2, the precision.
N	The number of samples to generate.
rseed	The random number seed. Defaults to 44 to reproduce the results in the book \insertCiteSahubook;textualbmstdr.

Value

A list containing the exact values of pdic, dic, pdicalt, dicalt, pwaic1, waic1, pwaic2, waic2, gof, penalty and pmcc. Also prints out the posterior mean and variance. @references \insertAllCited

Examples

Bmchoice()
b1 <- Bmchoice(case="Exact.sigma2.known")
b2 <- Bmchoice(case="MC.sigma2.known")
d1 <- Bmchoice(case="MC.sigma2.unknown")
d2 <- Bmchoice(y=rt(100, df=8),  kprior=1, prior.M=1)

sujit-sahu/bmstdr documentation built on June 1, 2025, 7:12 p.m.