bbricks (version 0.1.4)

MetropolisHastings: Metropolis-Hastings sampler

Description

sample xhat from the target distribution p(xhat), with given proposal distribution q(xhat|x), the acceptance rate is: min( 1 , dp(xhat)/dp(x) * dq(x|xhat)dq(xhat|x) ) where dp() is the density function of the target distribution, dq() the the density function of the proposal distribution. A new sample xhat is drawn from the sampler of the proposal distribution rq(). See examples.

Usage

MetropolisHastings(nsamples, xini, dp, dq, rq)

Arguments

nsamples

integer, number of samples to draw

xini

initial sample, the chain of samples starts from here. xini must be a matrix of one row, or a numeric vector that will be converted to a matrix of one row.

dp

function(x), the LOG density function of the target distribution log dp(x), DON'T FORGET THE LOG.

dq

function(xhat,x), the LOG density function of the proposal distribution log dq(xhat | x), DON'T FORGET THE LOG.

rq

function(x), the generator of the proposal distribution rq(xhat | x).

Value

a matrix of nsamples rows.

Examples

# NOT RUN {
## example1: independent Metropolis-Hastings algorithm, get 5000 samples from Beta(2.7,6.3)
## with independent uniform proposal U(0,1), and independent normal proposal N(0.5,1).

## step1: define p() and q()
dp <- function(x) if(x>0&x<1) dbeta(x,2.7,6.3,log = TRUE) else -Inf
dq1 <- function(xnew,x) 0               #uniform proposal log density
dq2 <- function(xnew,x) dnorm(xnew,0.5,1) #normal proposal log density
rq1 <- function(x) runif(1,0,1)           #uniform proposal sampler
rq2 <- function(x) rnorm(1,0.5,1)         #normal proposal sampler
## step2: get 5000 samples, with two different proposals
X1 <- MetropolisHastings(nsamples = 5000,xini = runif(1,0,1),dp=dp,dq=dq1,rq=rq1)
X2 <- MetropolisHastings(nsamples = 5000,xini = runif(1,0,1),dp=dp,dq=dq2,rq=rq2)
## step3: plot the result, calculate acceptance rate
sum(diff(X1)!=0)/nrow(X1)                 #the acceptance rate of uniform proposal
sum(diff(X2)!=0)/nrow(X2)                 #the acceptance rate of normal proposal
## Clearly Uniform, compare to Normal, can better resemble Beta, so the acceptance rate is higher
## plot the results
hist(X1)
hist(X2)
hist(rbeta(5000,2.7,6.3))

## example2: independent Metropolis-Hastings algorithm, sample from an improper distribution
## p(x) = -|x|+1, where -1<x<1, with independent uniform proposal U(-1,1)

## step1: define p() and q()
dp <- function(x) log(-abs(x)+1)        #log dp
dq <- function(xnew,x) 1
rq <- function(x) runif(1,-1,1)         #make sure -1<x<1
## step2: get 5000 samples
X <- MetropolisHastings(nsamples = 5000,xini = runif(1,-1,1),dp=dp,dq=dq,rq=rq)
## step3: plot the result, calculate acceptance rate
hist(X)
sum(diff(X)!=0)/nrow(X)                 #the acceptance rate

## example3: random walk Metropolis-Hastings algorithm, sample from a
## normal mixture 0.2*N(1,1)+0.8*N(-5,1), with symmetric proposal xhat ~ U(x-l,x+l),
## compare different values of l.

## step1: define p() and q()
dp <- function(x) log(dnorm(x,1,1)*0.2+dnorm(x,-5,1)*0.8)
## a symmetric proposal has no influence to the acceptance rate, so a constant function
## would suffice.
dq <- function(xnew,x) 1
rq1 <- function(x) runif(1,x-0.01,x+0.01)
rq2 <- function(x) runif(1,x-2,x+2)
## step2: get 5000 samples
X1 <- MetropolisHastings(nsamples = 5000,xini = rnorm(1),dp=dp,dq=dq,rq=rq1)
X2 <- MetropolisHastings(nsamples = 50000,xini = rnorm(1),dp=dp,dq=dq,rq=rq2)
## step3: plot the result, calculate acceptance rate
sum(diff(X1)!=0)/nrow(X1)
sum(diff(X2)!=0)/nrow(X2)
## plot the results
hist(X1,xlim = c(-10,5))
hist(X2,xlim = c(-10,5))
hist(c(rnorm(1000,1,1),rnorm(4000,-5,1)),xlim = c(-10,5))

## note that X1 has a higher acceptance rate comparing to X2, though it performs poorer.
## So we use Kolmogorov-Smirnov to test the real performance of X1 and X2:
ks.test(jitter(X1),c(rnorm(1000,1,1),rnorm(4000,-5,1))) #ks.test() assumes continuous
## samples doesn't contain equal values, otherwise there will be a warning.so use jitter() to
## remove the equals
ks.test(jitter(X2),c(rnorm(1000,1,1),rnorm(4000,-5,1)))
## it turns out that even though X2 looks better from the histogram, it still doesn't
## pass the KS test.

## example4: hybrid Metropolis-Hastings algorithm, questions same as previous example,
## but use a mixutre proposal instead.

## we use mixture proposal to capture both the local and global areas of the target distribution
## step1: define p() and q()
dp <- function(x) log(dnorm(x,1,1)*0.2+dnorm(x,-5,1)*0.8)
## a symmetric proposal has no influence to the acceptance rate,
## so a constant function would suffice.
dq <- function(xnew,x) 1
##70% local, 30% global
rq <- function(x) if(runif(1)<0.7) runif(1,x-0.1,x+0.1) else runif(1,x-3,x+3)
## step2: get 5000 samples
X <- MetropolisHastings(nsamples = 5000,xini = rnorm(1),dp=dp,dq=dq,rq=rq)
## step3: plot the result, calculate acceptance rate
sum(diff(X)!=0)/nrow(X)
## plot the results
hist(X,xlim = c(-10,5))
hist(c(rnorm(1000,1,1),rnorm(4000,-5,1)),xlim = c(-10,5))

## perform the KS test again, this time it says there's no significance difference between the MH
## and the real samples. ks.test() assumes continuous samples doesn't contain equal values,
## otherwise there will be a warning.so use jitter() to remove the equals
ks.test(jitter(X),c(rnorm(1000,1,1),rnorm(4000,-5,1))) 
# }