Metropolis algorithm + Rhat (PSRF) demonstration

ggplot2 and gridExtra are used for plotting, tidyr for manipulating data frames

library(ggplot2)
theme_set(theme_minimal())
library(gridExtra)
library(tidyverse)
library(gganimate)
library(MASS)
library(rstan)
library(rprojroot)
root<-has_file(".BDA_R_demos_root")$make_fix_file()

Parameters of a Normal distribution used as a toy target distribution

y1 <- 0
y2 <- 0
r <- 0.8
S <- diag(2)
S[1, 2] <- r
S[2, 1] <- r

Sample from the toy distribution to visualize 90% HPD interval with ggplot's stat_ellipse()

dft <- data.frame(mvrnorm(100000, c(0, 0), S))

Load pre-run Metropolis chains.

Since, implementation of the Metropolis algorithm is one of the exercises, we load here pre-computed chains and Rhat-values

The proposal distribution was intentionally selected to be slightly too small, to better illustrate convergence diagonstics

tts' contains draws,p1' and 'p2' contain Rhat values for t1 and t2 using 50% warm-up, `pp1' and 'pp2' contain Rhat values for t1 and t2 using 10% warm-up, Rhat-values have been computed for each time-step.

load(root("demos_ch11","demo11_4.RData"))

Transform the first s1 rows of the data into a 'tidy' format for plotting

s1 <- 50
dfs1 <- data.frame(iter = 1:s1, tts[1:s1, 1, ]) %>%
  gather(chain, th1, -iter) %>%
  within({th2 <- c(tts[1:s1, 2, ])         # xl and yl specify the previous
          th2l <- c(th2[1], th2[-length(th2)])   # draw in the chain for
          th1l <- c(th1[1], th1[-length(th1)])}) # plotting

Fix the incorrect lagged values, the lagged value of the first draw in the chain (for plotting) is the value itself (instead of the last value of the previous chain)

sind <- 0:9*s1+1
dfs1[sind, c('th1l','th2l')] <- dfs1[sind, c('th1','th2')]

Another data frame with all draws

inds2 <- 1:10000
dfs2 <- data.frame(iter = inds2, tts[inds2, 1, ]) %>%
  gather(chain, theta1, -iter) %>%
  within(theta2 <- c(tts[inds2, 2, ])) %>%
  gather(var, val, -iter, -chain)

Third data frame with Rhat values

indsp <- seq(10, length(p1), 10)
dfp <- data.frame(iter = indsp,
                  theta1 = p1[indsp],
                  theta2 = p2[indsp]) %>%
  gather(var,rhat,-iter) 

Construct a 2d-plot of the 50 first iterations of the chains

frame = rep(1:s1, 10)
chains1 <- ggplot(data = dfs1) +
  geom_segment(aes(x = th1, xend = th1l, y = th2, yend = th2l, color = chain,
                   group = chain)) +
  geom_point(data = dfs1[sind, ], aes(x = th1, y = th2, color = chain)) +
  stat_ellipse(data = dft, aes(x = X1, y = X2), level = 0.9, color = 'black') +
  coord_cartesian(xlim = c(-4, 4), ylim = c(-4, 4)) +
  labs(x = 'theta1', y = 'theta2') +
  scale_color_discrete(guide = FALSE)

Animate s1 first iterations of the chains. At some points some of the chains seem to halt for

#  a moment. What really happens at that point is that

they draw possibly a few points that are rejected (rejected points not shown) and thus the chain is not moving

animate(chains1 + 
          transition_reveal(along=iter) + 
          shadow_trail(1/s1))
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