# NOT RUN {
# Bioassay Data Example
# Cox, D.R. and Snell, E.J. (1989). Analysis of Binary Data. 2nd ed.
# Chapman and Hall. p. 7
# In this example there are 150 subjects at 5 different stimulus levels,
# 30 at each level.
y<-c(rep(0,30-2),rep(1,2),
rep(0,30-8),rep(1,8),
rep(0,30-15),rep(1,15),
rep(0,30-23),rep(1,23),
rep(0,30-27),rep(1,27))
x<-c(rep(0,30),
rep(1,30),
rep(2,30),
rep(3,30),
rep(4,30))
# Initial state
state <- NULL
# MCMC parameters
nburn<-5000
nsave<-5000
nskip<-10
ndisplay<-1000
mcmc <- list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay,
tune=1.1)
# Prior distribution
prior <- list(beta0=rep(0,2), Sbeta0=diag(10000,2))
# Fit a logistic regression model
fit1 <- Pbinary(y~x,link="logit",prior=prior,
mcmc=mcmc,state=state,status=TRUE)
fit1
# Fit a probit regression model
fit2 <- Pbinary(y~x,link="probit",prior=prior,
mcmc=mcmc,state=state,status=TRUE)
fit2
# Fit a cloglog regression model
fit3 <- Pbinary(y~x,link="cloglog",prior=prior,
mcmc=mcmc,state=state,status=TRUE)
fit3
# Fit a cauchy regression model
fit4 <- Pbinary(y~x,link="cauchy",prior=prior,
mcmc=mcmc,state=state,status=TRUE)
fit4
# }
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