多次运行函数

Question

我有我写的 R 代码：

flipCoinPoissonTimes <- function() {
    lambda <- 5  # initial parameter for the Poisson distribution
    num_flips <- rpois(1, lambda)  # generate a random number from a Poisson distribution

p <- 0.5  # initial probability of heads
set <- 1  # initial set number
results <- data.frame(set=integer(), turn_number=integer(), value_of_p=numeric(), result=character(), lambda=numeric(), num_flips=integer())

while(TRUE) {
    for (turn_number in 1:num_flips) {
        flip <- runif(1)  # generate a random number between 0 and 1
        if (flip < p) {
            results <- rbind(results, data.frame(set=set, turn_number=turn_number, value_of_p=p, result="Heads", lambda=lambda, num_flips=num_flips))
            return(results)  # stop once the first head is observed
        } else {
            results <- rbind(results, data.frame(set=set, turn_number=turn_number, value_of_p=p, result="Tails", lambda=lambda, num_flips=num_flips))
            p <- p / 2  # reduce the probability of heads by half
        }
    }
    # if no heads were observed in this set, generate a new Poisson random number centered around the previous num_flips
    lambda <- num_flips
    num_flips <- rpois(1, lambda)
    p <- 0.5  # reset the probability of heads
    set <- set + 1  # increment the set number
}
}


results <- flipCoinPoissonTimes()
print(results)

results$iteration = 1

results$total_turns = 1:nrow(results)

final = results

我尝试运行此代码 100 次并将所有结果存储在单个垂直数据框中（包含所有列，包括迭代和总转数）。

我尝试了这段代码：

> replicate(100, flipCoinPoissonTimes())

但它没有产生正确的结果。

有人可以告诉我如何正确地做吗？

Answer 1

我自己尝试使用 lapply 解决这个问题：

flipCoinPoissonTimes <- function(iteration) {
    lambda <- 5  # initial parameter for the Poisson distribution
    num_flips <- rpois(1, lambda)  # generate a random number from a Poisson distribution

p <- 0.5  # initial probability of heads
set <- 1  # initial set number
results <- data.frame(set=integer(), turn_number=integer(), value_of_p=numeric(), result=character(), lambda=numeric(), num_flips=integer(), iteration=integer())

while(TRUE) {
    for (turn_number in 1:num_flips) {
        flip <- runif(1)  # generate a random number between 0 and 1
        if (flip < p) {
            results <- rbind(results, data.frame(set=set, turn_number=turn_number, value_of_p=p, result="Heads", lambda=lambda, num_flips=num_flips, iteration=iteration))
            return(results)  # stop once the first head is observed
        } else {
            results <- rbind(results, data.frame(set=set, turn_number=turn_number, value_of_p=p, result="Tails", lambda=lambda, num_flips=num_flips, iteration=iteration))
            p <- p / 2  # reduce the probability of heads by half
        }
    }
    # if no heads were observed in this set, generate a new Poisson random number centered around the previous num_flips
    lambda <- num_flips
    num_flips <- rpois(1, lambda)
    p <- 0.5  # reset the probability of heads
    set <- set + 1  # increment the set number
}
}

现在，我启动该功能：

# lapply
all_results <- do.call(rbind, lapply(1:100, function(i) {
    result <- flipCoinPoissonTimes(i)
    result$total_turns <- 1:nrow(result)
    return(result)
}))

作为奖励，我绘制了结果（保留每次迭代的最后一行）：

library(dplyr)

last_rows <- all_results %>%
  group_by(iteration) %>%
  filter(row_number() == n())


# Reshape 
all_results_long <- last_rows %>%
    pivot_longer(cols = c(turn_number, value_of_p, lambda, num_flips, total_turns), 
                 names_to = "variable", 
                 values_to = "value")

# Plot 
ggplot(all_results_long, aes(x = log(value), color = variable, fill = variable)) +
    geom_density(alpha = 0.3) +
    labs(title = "Density Distributions of Various Columns",
         x = "Value",
         y = "Density") +
    theme_minimal()