mhReffects {nclbayes} | R Documentation |
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.
mhReffects(N, initial, intau, innu, priorparam, m, n, ybar, s, show = TRUE, innLogscale = FALSE)
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. |
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)