汇总数据置信区间的计数器循环

Question

我正在尝试使用 for 循环作为重复计数器将摘要数据添加到测试样本。我尝试使用 data.frame、矩阵和向量将我的数据推出 for 循环并填充表格。我得到的最好的结果是填充向量中的一整列并完成数据框中除一行之外的所有列。

#try empty vector to populate 
large.sample.df <- vector(mode = "double", length = 1000)

#try matrix to populate 
large.matrix <- matrix(nrow = 1000, ncol = 3)
matrix.names <- c("mean", "lwr", "upr")
colnames(large.matrix) <- matrix.names

#Try dataframe to populate
large.df <- data.frame(mean="", lwr="", upr="")

#set total length
n <- length(large.sample.df)

#use functions to calculate confidence interval
lwr.ci <- function(a) (mean(a) - 1.96 * (sd(a)/sqrt(length(a))))
upp.ci <- function(a) (mean(a) + 1.96 * (sd(a)/sqrt(length(a))))

#Start new seed count
set.seed(1234)

#begin for loop for mean, lwr, upr CI
for (i in 1:n) {
  large.sample <- rgamma(n = 1000, shape = 4, rate = 2)
  large.df$mean[i] <- mean(large.sample)
  large.df$lwr[i] <- lwr.ci(large.sample)
  large.df$upr[i] <- upp.ci(large.sample)
  }

Answer 1

这里有两种方法可以得到你想要的东西。首先我们要区分样本大小和样本数量：

set.seed(1234)
n <- 1000
samples <- 10  # Keep this small for testing and then increase it
s <- 4
r <- 2

首先是你的循环方法：

results <- data.frame(mean=NA, lwr=NA, upr=NA)   # Not "" which makes the variables character strings
set.seed(1234)
for (i in 1:samples) {
    x <- rgamma(n, shape = s, rate = r)
    mn <- mean(x)
    sder <- sd(x)/sqrt(n)
    lwr <- mn - 1.96 * sder
    upr <- mn + 1.96 * sder
    results[i, ] <- c(mn, lwr, upr) 
}
results
#           mean         lwr         upr
# 1  2.015193688 1.952431714 2.077955663
# 2  2.024218250 1.962404608 2.086031891
# 3  2.008401293 1.948363928 2.068438658
# 4  1.993061142 1.932020588 2.054101696
# 5  1.975824831 1.912961486 2.038688176
# 6  1.983761126 1.923583927 2.043938325
# 7  1.983166350 1.924890819 2.041441880
# 8  1.975453269 1.915336118 2.035570420
# 9  1.976118333 1.915025748 2.037210918
# 10 2.044088839 1.983435628 2.104742050

现在使用

replicate

confint <- function(n, s, r) {
    x <- rgamma(n, shape = s, rate = r)
    mn <- mean(x)
    sder <- sd(x)/sqrt(n)
    lwr <- mn - 1.96 * sder
    upr <- mn + 1.96 * sder
    return(c(mean=mn, lwr=lwr, upr=upr))
}
confint(n, s, r)   # Test the function
#        mean         lwr         upr 
# 1.974328366 1.914003710 2.034653023 
set.seed(1234)
results <- replicate(samples, confint(n, s, r))
results <- t(results)
results
#              mean         lwr         upr
#  [1,] 2.015193688 1.952431714 2.077955663
#  [2,] 2.024218250 1.962404608 2.086031891
#  [3,] 2.008401293 1.948363928 2.068438658
#  [4,] 1.993061142 1.932020588 2.054101696
#  [5,] 1.975824831 1.912961486 2.038688176
#  [6,] 1.983761126 1.923583927 2.043938325
#  [7,] 1.983166350 1.924890819 2.041441880
#  [8,] 1.975453269 1.915336118 2.035570420
#  [9,] 1.976118333 1.915025748 2.037210918
# [10,] 2.044088839 1.983435628 2.104742050

两种方法都一致。