如何将此 for 循环的结果保存为向量而不是单个值（在 R 中）？

Question

我是 for 循环的新手，无法以我想要的方式保存 for 循环的结果。

我当前正在运行的循环如下所示：

# Setup objects
n = 100
R = (1:1000)
P = seq(-.9, .9, .1)
betahat_OLS = rep(NA, 1000)
Bhat_OLS = rep(NA, 19)

# Calculate betahat_OLS for each p in P and each r in R
for (p in P) {
  for (r in R) {
    # Simulate data
    v = rnorm(n)
    e = rnorm(n)
    z = rnorm(n)
    u = p*v+e
    x = z+v
    y = 0*x+u
    #Calculate betahat_OLS
    betahat_OLS[r] = sum(x*y)/sum(x^2)
  }
  #Calculate Bhat_OLS
  Bhat_OLS = sum(betahat_OLS)/1000-0
}

# Make a scatterplot with p on the x-axis and Bhat_OLS on the y-axis
plot(P, Bhat_OLS)

循环似乎工作正常，除了我想最终得到 19 个 Bhat_OLS 值并且目前只得到 1 个值。 我想为 P 中的每个 p 值都有一个 Bhat_OLS 值，以便我可以针对 p 进行 plot Bhat_OLS； 我只是不知道如何告诉 R 这样做。

任何帮助将不胜感激！

Answer 1

您可以将结果写入具有两列的数据框中，其中包含P和Bhat_OLS 。

# Setup objects
n = 100
R = (1:1000)
P = seq(-.9, .9, .1)
betahat_OLS = rep(NA, 1000)
Bhat_OLS = rep(NA, 19)

# initialize result data frame
results <- data.frame(matrix(ncol = 2, nrow = 0, 
                      dimnames = list(NULL, c("P", "Bhat_OLS"))))

# Calculate betahat_OLS for each p in P and each r in R
for (p in P) {
    for (r in R) {
        # Simulate data
        v = rnorm(n)
        e = rnorm(n)
        z = rnorm(n)
        u = p*v+e
        x = z+v
        y = 0*x+u
        #Calculate betahat_OLS
        betahat_OLS = sum(x*y)/sum(x^2)
    }
    #Calculate Bhat_OLS
    Bhat_OLS = sum(betahat_OLS)/1000-0
    
    # insert P and Bhat_OLS into results
    results[nrow(results) + 1,] = c(p, Bhat_OLS)
}

# Make a scatterplot with p on the x-axis and Bhat_OLS on the y-axis
plot(results$P, results$Bhat_OLS)

Answer 2

您循环概率的事实使索引变得困难。 您可以循环遍历seq(P)和子集P[i] 。 此外，最后你需要Bhat_OLS[i] 。 然后它工作。

# Setup objects
n <- 100
R <- (1:1000)
P <- seq(-.9, .9, .1)
betahat_OLS <- rep(NA, length(R))
Bhat_OLS <- rep(NA, length(P))

set.seed(42)  ## for sake of reproducibility

# Calculate betahat_OLS for each p in P and each r in R
for (i in seq(P)) {
  for (r in R) {
    # Simulate data
    v <- rnorm(n)
    e <- rnorm(n)
    z <- rnorm(n)
    u <- P[i]*v + e
    x <- z + v
    y <- 0*x + u
    #Calculate betahat_OLS
    betahat_OLS[r] <- sum(x*y)/sum(x^2)
  }
  #Calculate Bhat_OLS
  Bhat_OLS[i] <- sum(betahat_OLS)/1000 - 0
}

# Make a scatterplot with p on the x-axis and Bhat_OLS on the y-axis
plot(P, Bhat_OLS, xlim=c(-1, 1))

替代解决方案vapply

以更 R-ish 的方式（现在它更 C-ish），您可以在 function sim()中定义模拟，并将vapply用于外部循环。 （实际上也适用于内部循环，但我已经对其进行了测试，这样它会更快。）

sim <- \(p, n=100, R=1:1000) {
  r <- rep(NA, max(R))
  for (i in R) {
    v <- rnorm(n)
    e <- rnorm(n)
    z <- rnorm(n)
    u <- p*v + e
    x <- z + v
    y <- 0*x + u
    r[i] <- sum(x*y)/sum(x^2)
  }
  return(sum(r/1000 - 0))
}

set.seed(42)
Bhat_OLS1 <- vapply(seq(-.9, .9, .1), \(p) sim(p), 0)

stopifnot(all.equal(Bhat_OLS, Bhat_OLS1))

笔记：

R.version.string
# [1] "R version 4.1.2 (2021-11-01)"