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为了帮助我理解贝叶斯更新，我一直在使用来自 bayesianbiologist 的代码。由于我必须学习如何创建动画情节，我认为创建更新的动画情节将是一个有趣的练习。事实证明，这比预期的要艰难。从Rob Hyndman 的博客中获取灵感，我尝试创建以下内容：

library(animation)
setwd("~/Dropbox/PriorUpdating") #set working directory

## Simulate Bayesian Binomial updating

sim_bayes<-function(p=0.5,N=10,y_lim=15,prior_a=1,prior_b=1)
{
   print(paste("The prior expectation of p is ",prior_a/(prior_a+prior_b)))
   success<-0
   curve(dbeta(x,prior_a,prior_b),xlim=c(0,1),ylim=c(0,y_lim),xlab='p',ylab='Posterior Density',lty=2)
   legend('topright',legend=c('Prior','Updated Posteriors','Final Posterior'),lty=c(2,1,1),col=c('black','black','red'))

  for(i in 1:N)
     {

        if(runif(1,0,1)<=p) success<-success+1 #this is where we see if there is a "success"

      curve(dbeta(x,success+prior_a,(i-success)+prior_b),add=TRUE) #plot updated
      }
curve(dbeta(x,success+prior_a,(i-success)+prior_b),add=TRUE,col='red',lwd=1.5) #plot final posterior
}

oopt = ani.options(interval = 0)
saveMovie(sim_bayes(p=0.6,N=90,prior_a=1,prior_b=1),interval=0.1,width=580,height=400)
ani.options(oopt)

然而，这只产生了最终的情节。所以我想我会尝试输出绘图的所有 PDF。

library(animation)
setwd("~/Dropbox/PriorUpdating") #set working directory

## Simulate Bayesian Binomial updating

sim_bayes<-function(p=0.5,N=10,y_lim=15,prior_a=1,prior_b=1)
{
  print(paste("The prior expectation of p is ",prior_a/(prior_a+prior_b)))
  success<-0



  curve(dbeta(x,prior_a,prior_b),xlim=c(0,1),ylim=c(0,y_lim),xlab='p',ylab='Posterior Density',lty=2)
  legend('topright',legend=c('Prior','Updated Posteriors','Final Posterior'),lty=c(2,1,1),col=c('black','black','red'))



  for(i in 1:N)
  {
    pdf(paste("posterior",i,".pdf",sep=""),height=4,width=6.5)

    if(runif(1,0,1)<=p) success<-success+1 #this is where we see if there is a "success"

    curve(dbeta(x,success+prior_a,(i-success)+prior_b),add=TRUE) #plot updated

    #print(paste(success,"successes and ",i-success," failures"))
    dev.off()
  }
  pdf(paste("posterior_final",".pdf",sep=""),height=4,width=6.5)
  curve(dbeta(x,success+prior_a,(i-success)+prior_b),add=TRUE,col='red',lwd=1.5) #plot final posterior
  dev.off()
}

但是，这给了我以下错误

Error in plot.xy(xy.coords(x, y), type = type, ...) : 
  plot.new has not been called yet

我尝试在某些点插入 plot.new() ，但我认为这与图的附加性质相冲突。

有没有人知道如何使这些方法中的一种或两种正常工作？虽然这对我来说是一个玩具示例，但我需要绘制一些更有趣的动画，其中了解如何为情节设置动画将是有用/必要的。

谢谢你的帮助！

4

2 回答 2

1

就像 MatthewK 说的，你打错了电话pdf。但是，这应该可以让您启动并运行：

sim_bayes <- function(p=0.5, N=10, y_lim=15, prior_a=1, prior_b=1) {
    success <- 0
    for (i in 1:N) {
        pdf(paste("posterior",i,".pdf",sep=""), height=4, width=6.5)

        if (runif(1,0,1) <= p)
            success<-success + 1

        # Start a new plot.
        curve(dbeta(x,prior_a,prior_b), lty=2,
              xlim=c(0,1), ylim=c(0,y_lim), xlab='p', ylab='Posterior Density')
        # Update plot.
        curve(dbeta(x,success+prior_a, (i-success) + prior_b), add=TRUE)

        legend('topright',
               legend=c('Prior','Updated Posteriors','Final Posterior'),
               lty=c(2,1,1), col=c('black','black','red'))

        dev.off()
    }
}

# `x` had no visible binding in your implementation, so I took the following
# from the `dbeta` documentation example.
x <- seq(0, 1, length=21)
sim_bayes()
于 2012-08-19T08:37:23.737 回答
1

你只在这里制作一个情节。如果你使用add=TRUE你添加到当前的情节，而不是制作一个新的情节。因此删除应该有效：

sim_bayes<-function(p=0.5,N=10,y_lim=15,prior_a=1,prior_b=1)
{
   print(paste("The prior expectation of p is ",prior_a/(prior_a+prior_b)))
   success<-0
   curve(dbeta(x,prior_a,prior_b),xlim=c(0,1),ylim=c(0,y_lim),xlab='p',ylab='Posterior Density',lty=2)
   legend('topright',legend=c('Prior','Updated Posteriors','Final Posterior'),lty=c(2,1,1),col=c('black','black','red'))

  for(i in 1:N)
     {

        if(runif(1,0,1)<=p) success<-success+1 #this is where we see if there is a "success"

      curve(dbeta(x,success+prior_a,(i-success)+prior_b)) #plot updated
      }
curve(dbeta(x,success+prior_a,(i-success)+prior_b),col='red',lwd=1.5) #plot final posterior
}

测试： 

sim_bayes(p=0.6,N=90,prior_a=1,prior_b=1)

给出多个图

于 2012-08-19T08:44:05.280 回答