即使不滿足條件，函式內的回圈也會停止

我有點難過，我正在嘗試撰寫一個代碼來運行不斷增加的樣本量的蒙特卡羅模擬，直到滿足某些條件。首先，我知道的代碼確實有效：

##Step 0 - load packages##
library(tidyverse)
library(ggplot2)
library(ggthemes)

##Step 1 - Define number of cycles per simulation##
ncycles <- 250000

##Step 2 - Define function for generating volumes and checking proportion of failed cycles##
volSim <- function(ncycles){
  tols <- rnorm(ncycles,0,0.3) #Generate n unique tolerances
  vols <- 0 #Establish vols variable within function
  for (tol in 2:ncycles){ #for loop creates n unique volumes from tolerances
    vols[tol] <- 2.2 tols[tol]-tols[tol-1]
  }
  cell <- rnorm(1,3.398864,0.4810948) #Generate a unique threshold
  return(c(mean(vols>cell),mean(vols>cell*2),mean(vols>cell*20))) #Output a vector of failure rate
}

這可以正常作業并輸出三個值，這些值等于事件在閾值倍數上的比例。現在，對于不正常的位；

##Step 3 - Define a function to run multiple iterations of simulation and check convergence ##
regres <- function(ncycles){
    #Establish parameters used within function#
    converged <- FALSE
    fail_rate_5k <- 0
    se_5k <- 0
    ncells <- 0
    fail_rate_10k <- 0
    se_10k <- 0
    fail_rate_100k <- 0
    se_100k <- 0
    n <- 0
    while ((converged == FALSE & n<6) | n<4){
        n <- n 1
        res <- replicate(2^(n 5),volSim(ncycles))
        fail_rate_5k[n] <- mean(res[1,]>0)
        se_5k[n] <- sqrt(fail_rate_5k[n]*(1-fail_rate_5k[n])/2^(n 5))
        ncells[n] <- 2^(n 5)
        fail_rate_10k[n] <- mean(res[2,]>0)
        se_10k[n] <- sqrt(fail_rate_10k[n]*(1-fail_rate_10k[n])/2^(n 5))
        fail_rate_100k[n] <- mean(res[3,]>0)
        se_100k[n] <- sqrt(fail_rate_100k[n]*(1-fail_rate_100k[n])/2^(n 5))
        if((fail_rate_5k[n] <= 0 | se_5k[n] < 0.5*fail_rate_5k[n]) & 
        (fail_rate_10k[n] <= 0 | se_10k[n] < 0.5*fail_rate_10k[n]) &
        (fail_rate_100k[n] <= 0 | se_100k[n] < 0.5*fail_rate_100k[n])){
            converged <- TRUE}
        else {converged <- FALSE}
    return(data.frame(k5 = fail_rate_5k, se_k5 = se_5k, ncells_k5 = ncells, k10 = fail_rate_10k, se_k10 = se_10k, ncells_k10 = ncells, k100 = fail_rate_100k, se_k100 = se_100k, ncells_k100 = ncells))}
    }

目的是模擬將在增加樣本量時重復，直到所有失敗率（5k、10k、100k）的標準誤差小于失敗率的一半，或者失敗率本身為零（以避免除以零設想）。兩個警告是模擬必須至少運行四次（while 回圈中的 n<4 條件），并且最多在六次后停止。

Now, if I run the code within the regres function in isolation (with ncycles set to 250000), I generate a nice data frame with 5 rows, I can see that n = 5, converged = TRUE, and everything else that I expect to be happening within the function just fine. If I run result <- regres(ncycles) however, it outputs a single row data frame every time. The while loop is stopping at n=1 despite the n<4 condition. I cannot for the life of me figure out why the behaviour is different when the function is called from when the code inside it is run in isolation.

While I'm really looking to find out why this method is not working, if the method itself is completely outlandish I'm open to using a different approach entirely too.

uj5u.com熱心網友回復：

您的 return 陳述句在while回圈中。它將在第一次迭代結束時回傳 data.frame（基本上是break在它甚至檢查條件之前）

嘗試：

 ...
      converged <- TRUE}
    else {converged <- FALSE}
  }
  return(data.frame(k5 = fail_rate_5k, se_k5 = se_5k, ncells_k5 = ncells, k10 = fail_rate_10k, se_k10 = se_10k, ncells_k10 = ncells, k100 = fail_rate_100k, se_k100 = se_100k, ncells_k100 = ncells))
}

標籤：r

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