On Wed, 2004-09-15 at 03:27, HALL, MARK E wrote: > I've found > > Bayesian Methods: A Social and Behavioral Sciences Approach > by Jeff Gill > > useful as an introduction. The examples are written in R and S with generalized > scripts for doing > a variety of problems. (Though I never got change-point analysis to successfully in > R.) > Change point analysis? I haven't seen the book, but I read lecture handouts of one Bayesian course over here in Finland (Antti Penttinen, Jyväskylä), and translated his example to R during one (rare) warm summer day in a garden. So do you mean this (binary case):
> source("/mnt/flash/cb.update.R") > cb.update function (y, A=1, B=1, C=1, D=1, N=1200, burnin=200) { n <- length(y) lambda <- numeric(N) mu <- numeric(N) k <- numeric(N) lambda[1] <- A/(A+B) mu[1] <- C/(C+D) k[1] <- n/2 sn <- sum(y) for (i in 2:N) { kold <- k[i-1] sk <- sum(y[1:kold]) lambda[i] <- rbeta(1, A+sk, B + kold - sk) mu[i] <- rbeta(1, C + sn - sk, D + n - sn + sk - kold ) knew <- sample(n-1, 1) sknew <- sum(y[1:knew]) r <- (sknew - sk) * (log(lambda[i])-log(mu[i]))-(knew-kold)*(lambda[i]-mu[i]) if(min(0,r) > log(runif(1))) k[i] <- knew else k[i] <- k[i-1] } out <- cbind(lambda, mu, k) out[(burnin+1):N, ] } > y <- c(rbinom(60, 1, 0.8), rbinom(40, 1, 0.3)) > uh <- cb.update(y, N=5200) > colMeans(uh) lambda mu k 0.8189303 0.4169367 59.0770000 > mean(y[1:60]) [1] 0.7833333 > mean(y[41:100]) [1] 0.45 > plot(density(uh[,1])) > plot(density(uh[,2])) > plot(table(uh[,3]), type="h") This was off-topic. So something about business: isn't the (Win)BUGS author working with a R port? cheers, jari oksanen -- Jari Oksanen -- Dept Biology, Univ Oulu, 90014 Oulu, Finland email [EMAIL PROTECTED], homepage http://cc.oulu.fi/~jarioksa/ ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html