# demo/Chapter.6.10.R In LearnBayes: Functions for Learning Bayesian Inference

```#############################################################
# Section 6.10 Analysis of the Stanford Heart Transplant Data
#############################################################

library(LearnBayes)

data(stanfordheart)

start=c(0,3,-1)
laplacefit=laplace(transplantpost,start,stanfordheart)
laplacefit

proposal=list(var=laplacefit\$var,scale=2)
s=rwmetrop(transplantpost,proposal,start,10000,stanfordheart)
s\$accept

par(mfrow=c(2,2))
tau=exp(s\$par[,1])
plot(density(tau),main="TAU")
lambda=exp(s\$par[,2])
plot(density(lambda),main="LAMBDA")
p=exp(s\$par[,3])
plot(density(p),main="P")

apply(exp(s\$par),2,quantile,c(.05,.5,.95))

S=readline(prompt="Type  <Return>   to continue : ")

par(mfrow=c(1,1))
t=seq(1,240)
p5=0*t; p50=0*t; p95=0*t
for (j in 1:240)
{ S=(lambda/(lambda+t[j]))^p
q=quantile(S,c(.05,.5,.95))
p5[j]=q[1]; p50[j]=q[2]; p95[j]=q[3]}
windows()
plot(t,p50,type="l",ylim=c(0,1),ylab="Prob(Survival)",
xlab="time")
lines(t,p5,lty=2)
lines(t,p95,lty=2)
```

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LearnBayes documentation built on March 19, 2018, 1:04 a.m.