gibbsReffects: Gibbs sampler for a one-way normal random effects model with...

In mas3321: Bayes normal linear model and basic MCMC

Description

Simulates realisations from the posterior distribution for the population mean, random effect means and precision components in a one-way normal random effects model with a semi-conjugate prior

Usage

  gibbsReffects(N, initial, priorparam, m, n, ybar, s)

Arguments

N	length of MCMC chain
initial	starting values for the population mean and the precision components
priorparam	prior parameters a,b,c,d,e,f
m	number of treatments
n	vector containing the number of observations on each treatment
ybar	vector containing the mean of observations on each treatment
s	vector containing the standard deviation of observations on each treatment

Examples

data(contamination)
n=tapply(contamination$acc,contamination$keyboard,length)
ybar=tapply(contamination$acc,contamination$keyboard,mean)
s=sqrt(tapply(contamination$acc,contamination$keyboard,var)*(n-1)/n)
mcmcAnalysis(gibbsReffects(N=100,initial=c(200,2e-5,1),priorparam=c(200,0.1,0.1,0.1,0.1,0.1),m=10,n=n,ybar=ybar,s=s))

mas3321 documentation built on May 2, 2019, 4:42 p.m.