蒙特卡洛模拟代码:在R中生成给定大小的样本 [英] Code for Monte Carlo simulation: generate samples of given size in R

问题描述

我首先使用以下代码生成了一个0到1之间的500个均匀分布的随机数样本:

I started by generating a sample of 500 uniformly-distributed random numbers between 0 and 1 using the code below:

set.seed(1234)
X<-runif(500, min=0, max=1)

现在，我需要编写一个伪代码，以生成10000个N = 500的样本用于MC模拟，计算我新创建的X的平均值，并将迭代次数和平均值存储在结果对象中.我从来没有尝试过，到目前为止，我有这个:

Now, I need to write a psuedocode that generates 10000 samples of N=500 for a MC simulation, compute the mean of my newly created X, and store the iteration number and mean value in a result object. I have never attempted this, and so far I have this:

n.iter <-(10000*500)
results <- matrix (0, n.iter, 4)

最后，一旦完成，我要运行它，然后获取应计样本均值的中值，均值和最小值/最大值，并将其保存到名为MC.table的数据帧中. (还要注意，上面我不知道为什么矩阵代码中有一个"4"-我正在处理前面的示例).任何建议或帮助将不胜感激.

Finally, once this is accomplished, I'm to run it, then obtain median, mean, and min/max of the accrued sample means and save them to a data frame called MC.table. (Also note, above, I have no idea why there's a "4" in the matrix code --- I'm working off of previous examples). Any advice or help would be greatly appreciated.

我有一个可能可行的示例，但我真的不了解它所发生的情况，因此请详细说明其有效性:

I have an example that may work, but I don't really understand what's going on with it, so please elaborate on its validity for this:

Ni <- 10000
n <- 500
c <- 0

for (i in n){
for (j in 1:Ni){
c <- c+ 1
d <- data.frame (x= , y= )
results [c,1] <- c
results [c,2] <- j
results [c,3] <- i
results [c,4] <- something( d$x, d$y)
rm (d) } }

如果您甚至可以花时间解释这意味着什么，那么对我有很大帮助！谢谢！

If you could even take the time to explain what that means, that'd go a long way to helping me! Thanks!

推荐答案

您可以尝试使用data.table，这是可以使用install.packages("data.table")安装的软件包.安装后，您将运行类似...

You could try using data.table, a package that can be installed using install.packages("data.table"). With that installed, you would run something like...

> require(data.table)
> dt <- data.table(x=runif(500*10000),iter=rep(1:500,each=10000))
                  # x iter
      # 1: 0.48293196    1
      # 2: 0.61935416    1
      # 3: 0.99831614    1
      # 4: 0.26944687    1
      # 5: 0.38027524    1
     # ---                
# 4999996: 0.11314160  500
# 4999997: 0.07958396  500
# 4999998: 0.97690312  500
# 4999999: 0.81670765  500
# 5000000: 0.62934609  500
> summaries <- dt[,list(mean=mean(x),median=median(x)),by=iter]
     # iter      mean    median
  # 1:    1 0.5005310 0.5026592
  # 2:    2 0.4971551 0.4950034
  # 3:    3 0.4977677 0.4985360
  # 4:    4 0.5034727 0.5052344
  # 5:    5 0.4999848 0.4971214
 # ---                         
# 496:  496 0.5013314 0.5048186
# 497:  497 0.4955447 0.4941715
# 498:  498 0.4983971 0.4910115
# 499:  499 0.5000382 0.4997024
# 500:  500 0.5009614 0.4988237
> min_o_means <- min(summaries$mean)
# [1] 0.4914826

我认为语法非常简单.您可能需要使用?(例如，?rep)查找某些功能.以#开头的行仅显示生成的对象.在data.tables中，:左侧的数字只是行号，而---表示在显示中跳过的行.

I think the syntax is fairly straightforward. You may want to look up some of the functions using ? (e.g., ?rep). The lines starting with # are just displaying the generated objects. In data.tables, the number to the left of the : is just the row number and --- indicates rows that are skipped in the display.

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