# mhReffects: Metropolis-Hastings algorithm for a one-way normal random... In mas3321: Bayes normal linear model 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

 ```1 2``` ``` 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` logical. If true then acceptance rate for the proposals will be given `innLogscale` logical. If true then proposals are made on a log scale

## Examples

 ```1 2 3 4 5``` ```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) ```

mas3321 documentation built on May 31, 2017, 1:50 a.m.