metrop(obj, initial, nbatch, blen = 1,.....)
  obj : an R function that evaluates the log unnormalized probability density
        of the desired equilibrium distribution of the Markov chain.
  return value 可以是 -Inf
  但不可以是Inf, NA or NaN

所以底下范例是OK
  h <- function(x) if (all(x >= 0) && sum(x) <= 1) return(1) else return(-Inf)
  out <- metrop(h, rep(0, 5), 1000)

若改成底下就出错了
  h <- function(x) if (all(x >= 0) && sum(x) <= 1) return(1) else return(Inf)
  out <- metrop(h, rep(0, 5), 1000)

谢谢~~请问所以是不是我还需要再加个判断语句判断h的可能结果?当我不用metrop 把向量i赋值代入方程h是有一个值的~
try<-c(a,b,c)
h<-function(try){
atoxity<-0.05
btoxity<-0.1
n<-1
x<-0
pie<-1-((1-atoxity^try[1])^(-try[3])+(1-btoxity^try[2])^(-try[3])-1)^(-1/try[3])
logl<-log((pie^x)*(1-pie)^(n-x))
logprior<-log(dgamma(try[1],0.5,0.5)*dgamma(try[2],0.5,0.5)*dgamma(try[3],0.1,0.1))
return (logl+logprior)
}
library(mcmc)
set.seed(528)
i<-c(3.334313e-01,5.218204e-02,2.324598e-14)
out <- metrop(h,i,100,blen = 1, nspac = 1, scale = 0.5)

你可以参考MCMC Package Example
demo.pdf来写log unnormalized posterior (log likelihood plus log prior) density function
#####
library(mcmc)
data(logit)
out <- glm(y ~ x1 + x2 + x3 + x4, data = logit, family = binomial(), x = TRUE)
summary(out)
#log unnormalized posterior (log likelihood plus log prior) density
lupost <- function(beta, x, y) {
eta <- as.numeric(x %*% beta)
logp <- ifelse(eta < 0, eta - log1p(exp(eta)), -log1p(exp(-eta)))
logq <- ifelse(eta < 0, -log1p(exp(eta)), -eta - log1p(exp(-eta)))
logl <- sum(logp[y == 1]) + sum(logq[y == 0])
return(logl - sum(beta^2)/8)
}

set.seed(42)
x <- out$x
y <- out$y
beta.init <- as.numeric(coefficients(out))
metropout <- metrop(lupost, beta.init, 1000, x = x, y = y)
names(metropout)

谢谢啊 实在不好意思 我看了demo 想请教一下 这两句话是什么意思呀 log unnormalized posterior density function 是不是就是函数外加上Log然后写成log likelihood plus log prior的样子呢 真心麻烦你了~~
logp <- ifelse(eta < 0, eta - log1p(exp(eta)), -log1p(exp(-eta)))
logq <- ifelse(eta < 0, -log1p(exp(eta)), -eta - log1p(exp(-eta)))

#------------------
# data
#------------------

y <- c(-0.91, 1.27, 0.85, 0.89, -0.22, 1.07, 2.25, -1.20, -0.95, -0.82)
n <- length(y)

#-----------------------------------------------------------------
# model states that
#
# [Y | mu, psi] i.i.d~ Normal(mu, 1 / psi),   i = 1, ..., n
#
# Parameter vector theta = (mu, psi), where mu is the expected value
# and psi is the precision of the normal population.
#
# use the improper prior with density proportional to 1/psi.
#--------------------------------------------------------------------
# Define functions for calculating  log-likelihood, log-prior, and
# log(unnormalized posterior)
#--------------------------------------------------------------------

# Log-likelihood
loglik <- function(theta) {
  sum(dnorm(log = TRUE, y, mean = theta[1], sd = 1 / sqrt(theta[2])))
}

# log-prior for the improper prior
logprior <- function(theta) (-log(theta[2]))

# Log unnormalized posterior
logpost <- function(theta) (loglik(theta) + logprior(theta))


##############
ifelse用法请看帮助
ifelse(test, yes, no)
     test an object which can be coerced to logical mode.
     yes return values for true elements of test.
     no return values for false elements of test.

logp <- ifelse(eta < 0, eta - log1p(exp(eta)), -log1p(exp(-eta)))

当 eta < 0(yes),logp = eta - log1p(exp(eta))
否则      (no) ,logp =  -log1p(exp(-eta))