嵌套for loop - mark-recapture

Question

感谢您抽出时间阅读这篇文章。

下面的代码创建了一个图表，该图表采用了100个样本，这些样本占总体(400)的5%到15%。

然而，我想要做的是在图表中添加另外两个部分。它看起来像这样：

从1-100个样本中抽取100个样本，这些样本占总体(400)的5%到15%。从101到200，取100个样本，占总人口的5%到15% (800)。从201-300中抽取100个样本，占总人口(300)的5%到15%。

我假设这需要一个嵌套的for循环。有谁有关于如何做到这一点的建议吗？

耽误您时间，实在对不起。柯尔斯腾

N <- 400
pop <- c(1:N)

lower.bound <- round(x = .05 * N, digits = 0)
lower.bound ## Smallest possible sample size

upper.bound <- round(x = .15 * N, digits = 0)
upper.bound ## Largest possible sample size

length.ss.interval <- length(c(lower.bound:upper.bound))
length.ss.interval ## total possible sample sizes, ranging form lower.bound
to upper.bound
sample(x = c(lower.bound:upper.bound),
       size = 1,
       prob = c(rep(1/length.ss.interval, length.ss.interval)))

n.samples <- 100

dat <- matrix(data = NA,
              nrow = length(pop),
              ncol = n.samples + 1)

dat[,1] <- pop

for(i in 2:ncol(dat)) {
  a.sample <- sample(x = pop,
                     size = sample(x = c(lower.bound:upper.bound),
                                   size = 1,
                                   prob = c(rep(1/length.ss.interval,
length.ss.interval))),
                     replace = FALSE)
  dat[,i] <- dat[,1] %in% a.sample
}
schnabel.comp <- data.frame(sample = 1:n.samples,
                            n.sampled = apply(X = dat, MARGIN = 2, FUN =
sum)[2:length(apply(X = dat, MARGIN = 2, FUN = sum))]
)
n.prev.sampled <- c(0, rep(NA, n.samples-1))
n.prev.sampled

n.prev.sampled[2] <- sum(ifelse(test = dat[,3] == 1 & dat[,2] == 1,
                                yes = 1,
                                no = 0))

for(i in 4:ncol(dat)) {
  n.prev.sampled[i-1] <- sum(ifelse(test = dat[,i] == 1 &
rowSums(dat[,2:(i-1)]) > 0,
                                    yes = 1,
                                    no = 0))
}

schnabel.comp$n.prev.sampled <- n.prev.sampled
schnabel.comp$n.newly.sampled <- with(schnabel.comp,
                                      n.sampled - n.prev.sampled)
schnabel.comp$cum.sampled <- c(0,
cumsum(schnabel.comp$n.newly.sampled)[2:n.samples-1])
schnabel.comp$numerator <- with(schnabel.comp,
                                n.sampled * cum.sampled)
schnabel.comp$pop.estimate <- NA

for(i in 1:length(schnabel.comp$pop.estimate)) {
  schnabel.comp$pop.estimate[i] <- sum(schnabel.comp$numerator[1:i]) /
sum(schnabel.comp$n.prev.sampled[1:i])
}

if (!require("ggplot2")) {install.packages("ggplot2"); require("ggplot2")}
if (!require("scales")) {install.packages("scales"); require("scales")}


small.sample.dat <- schnabel.comp

small.sample <- ggplot(data = small.sample.dat,
                       mapping = aes(x = sample, y = pop.estimate)) +
  geom_point(size = 2) +
  geom_line() +
  geom_hline(yintercept = N, col = "red", lwd = 1) +
  coord_cartesian(xlim = c(0:100), ylim = c(300:500)) +
  scale_x_continuous(breaks = pretty_breaks(11)) +
  scale_y_continuous(breaks = pretty_breaks(11)) +
  labs(x = "\nSample", y = "Population estimate\n",
       title = "Sample sizes are between 5% and 15%\nof the population") +
  theme_bw(base_size = 12) +
  theme(aspect.ratio = 1)

我的想法是使用以下代码创建一个嵌套的ifelse语句：

N.2 <- 800
N.3 <- 300
pop.2 <- c(401:N.2)
pop.3 <- c(801:N)

lower.bound.2 <- round(x = .05 * N.2, digits = 0)
upper.bound.2 <- round(x = .15 * N.2, digits = 0)

lower.bound.3 <- round(x = .05 * N.3, digits = 0)
upper.bound.3 <- round(x = .15 * N.3, digits = 0)

也许是某种排列...

dat <- imatrix(ifelse(n.samples ,= 100),
              yes = nrow = length(pop),
              no = ifelse(n.samples > 100 & > 201),
              yes = nrow = length(pop.2),
              no = nrow = length(pop.3),
              ncol = n.samples + 1)

Answer 1

这是你想要的吗？我在下面编写的函数mark_recapture接受四个参数(样本数量、样本的上下限和总体大小)，并输出一个矩阵，其中行表示总体中的个体，列表示样本。如果一个人在给定的样本中被捕获，它的得分为1，否则它的得分为0。定义该函数后，您只需使用3种不同的总体大小运行3次，即可得到3种不同的矩阵。

# define variables
num_samp <- 100
lower_sampsize <- 0.05
upper_sampsize <- 0.15

# define sampling function that outputs matrix
mark_recapture <- function (num_samp, pop_size, lower_sampsize, upper_sampsize) {

    # empty matrix
    mat <- matrix(0, pop_size, num_samp)

    # min and max sample size
    min <- ceiling(lower_sampsize*pop_size)
    max <- floor(upper_sampsize*pop_size)

    # vector of random sample sizes between min and max
    samp_sizes <- sample(min:max, num_samp, replace=TRUE)

    # draw the samples and fill in the matrix
    for (i in 1:num_samp) {mat[sample(1:pop_size, samp_sizes[i]),i] <- 1}

    # return matrix
    return(mat)
}

# do the sampling from the 3 populations
mat1 <- mark_recapture(num_samp=num_samp, pop_size=400, lower_sampsize=lower_sampsize, upper_sampsize=upper_sampsize)
mat2 <- mark_recapture(num_samp=num_samp, pop_size=800, lower_sampsize=lower_sampsize, upper_sampsize=upper_sampsize)
mat3 <- mark_recapture(num_samp=num_samp, pop_size=300, lower_sampsize=lower_sampsize, upper_sampsize=upper_sampsize)

虽然这超出了这个问题的范围，但我只想提一下，有专门的R包来分析和模拟标记重新捕获数据，例如multimark。只需搜索"CRAN mark recapture“，你就会找到许多选项。我建议仔细阅读这些内容，仔细考虑您在这里试图实现的目标，因为您可能正在尝试重新发明轮子。