mhReffects: Metropolis-Hastings algorithm for a one-way normal random...

In nclbayes: Functions, Demos and Examples of Bayesian Analysis and Basic MCMC

Description

Simulates realisations from the posterior distribution for the population mean and precision components in a one-way normal random effects model with a semi-conjugate prior. The method marginalises over the random effects and uses univariate normal or log normal random walk proposals for the precision components.

Usage

mhReffects(N, initial, intau, innu, priorparam, m, n, ybar, s, show = TRUE,
  innLogscale = FALSE)

Arguments

N	length of MCMC chain.
initial	starting values for the algorithm.
intau	standard deviation of normal random walk innovation for data precision parameter tau.
innu	standard deviation of normal random walk innovation for random effects precision parameter nu.
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.
show	a logical. If true then acceptance rate for the proposals will be given.
innLogscale	a logical. If TRUE then proposals are made on a log scale.

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(mhReffects(N=100,initial=c(200,2e-5,1),intau=1e-5,innu=7.9,
  priorparam=c(200,0.1,0.1,0.1,0.1,0.1),m=10,n=n,ybar=ybar,s=s,show=TRUE),rows=3)

nclbayes documentation built on May 2, 2019, 5:53 p.m.