R version 2.13.1 (2011-07-08)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-pc-linux-gnu (64-bit)

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> 
>  # test outfun (function)
> 
>  epsilon <- 1e-15
> 
>  library(mcmc)
> 
>  RNGkind("Marsaglia-Multicarry")
>  set.seed(42)
> 
>  n <- 100
>  rho <- 0.5
>  beta0 <- 0.25
>  beta1 <- 1
>  beta2 <- 0.5
> 
>  x1 <- rnorm(n)
>  x2 <- rho * x1 + sqrt(1 - rho^2) * rnorm(n)
>  eta <- beta0 + beta1 * x1 + beta2 * x2
>  p <- 1 / (1 + exp(- eta))
>  y <- as.numeric(runif(n) < p)
> 
>  out <- glm(y ~ x1 + x2, family = binomial())
> 
>  logl <- function(beta) {
+      if (length(beta) != 3) stop("length(beta) != 3")
+      beta0 <- beta[1]
+      beta1 <- beta[2]
+      beta2 <- beta[3]
+      eta <- beta0 + beta1 * x1 + beta2 * x2
+      p <- exp(eta) / (1 + exp(eta))
+      return(sum(log(p[y == 1])) + sum(log(1 - p[y == 0])))
+  }
> 
>  out.metro <- metrop(logl, coefficients(out), 1e3, scale = 0.01)
>  out.metro$accept
[1] 0.982
> 
>  out.metro <- metrop(out.metro, scale = 0.1)
>  out.metro$accept
[1] 0.795
> 
>  out.metro <- metrop(out.metro, scale = 0.5)
>  out.metro$accept
[1] 0.264
> 
>  apply(out.metro$batch, 2, mean)
[1] 0.06080257 1.42304941 0.52634149
> 
>  out.metro <- metrop(logl, as.numeric(coefficients(out)), 1e2,
+      scale = 0.5, debug = TRUE, outfun = function(x) c(x, x^2))
> 
>  niter <- out.metro$nbatch * out.metro$blen * out.metro$nspac
>  niter == nrow(out.metro$current)
[1] TRUE
>  niter == nrow(out.metro$proposal)
[1] TRUE
>  all(out.metro$current[1, ] == out.metro$initial)
[1] TRUE
>  all(out.metro$current[niter, ] == out.metro$final) |
+      all(out.metro$proposal[niter, ] == out.metro$final)
[1] TRUE
> 
>  .Random.seed <- out.metro$initial.seed
>  d <- ncol(out.metro$proposal)
>  n <- nrow(out.metro$proposal)
>  my.proposal <- matrix(NA, n, d)
>  my.u <- double(n)
>  ska <- out.metro$scale
>  for (i in 1:n) {
+      my.proposal[i, ] <- out.metro$current[i, ] + ska * rnorm(d)
+      if (is.na(out.metro$u[i])) {
+          my.u[i] <- NA
+      } else {
+          my.u[i] <- runif(1)
+      }
+  }
>  max(abs(out.metro$proposal - my.proposal)) < epsilon
[1] TRUE
>  all(is.na(out.metro$u) == is.na(my.u))
[1] TRUE
>  all(out.metro$u[!is.na(out.metro$u)] == my.u[!is.na(my.u)])
[1] TRUE
> 
>  my.curr.log.green <- apply(out.metro$current, 1, logl)
>  my.prop.log.green <- apply(out.metro$proposal, 1, logl)
>  all(is.na(out.metro$u) == (my.prop.log.green > my.curr.log.green))
[1] TRUE
>  foo <- my.prop.log.green - my.curr.log.green
>  max(abs(foo - out.metro$log.green)) < epsilon
[1] TRUE
> 
>  my.accept <- is.na(my.u) | my.u < exp(foo)
>  sum(my.accept) == round(n * out.metro$accept)
[1] TRUE
>  if (my.accept[niter]) {
+      all(out.metro$proposal[niter, ] == out.metro$final)
+  } else {
+      all(out.metro$current[niter, ] == out.metro$final)
+  }
[1] TRUE
> 
>  my.current <- out.metro$current
>  my.current[my.accept, ] <- my.proposal[my.accept, ]
>  my.current <- rbind(out.metro$initial, my.current[- niter, ])
>  max(abs(out.metro$current - my.current)) < epsilon
[1] TRUE
> 
>  my.path <- matrix(NA, n, d)
>  my.path[my.accept, ] <- out.metro$proposal[my.accept, ]
>  my.path[! my.accept, ] <- out.metro$current[! my.accept, ]
>  nspac <- out.metro$nspac
> 
>  my.path <- my.path[seq(nspac, niter, by = nspac), ]
> 
>  fred <- t(apply(my.path, 1, out.metro$outfun))
>  k <- ncol(fred)
> 
>  foom <- array(as.vector(t(fred)), c(k, out.metro$blen, out.metro$nbatch))
>  boom <- t(apply(foom, c(1, 3), mean))
> 
>  all(dim(boom) == dim(out.metro$batch))
[1] TRUE
>  max(abs(boom - out.metro$batch)) < epsilon
[1] TRUE
> 
>  goom <- cbind(my.path, my.path^2)
>  all(dim(goom) == dim(out.metro$batch))
[1] TRUE
>  max(abs(goom - out.metro$batch)) < epsilon
[1] TRUE
>