R 如何使数据集变量的柱状图与它重叠'；s正态分布更简单更奇特？

好的，我正在使用R中内置的经典虹膜数据集。 我使用的是可变萼片。宽度在鸢尾属“刚毛”亚种中。我绘制了概率直方图，它看起来可以遵循正态分布（钟形曲线），所以我收集了萼片宽度的平均值和萼片宽度的标准偏差，这就是绘制曲线所需的全部内容

正态/高斯分布公式：

f（x）=1/[σ*√（2*π）]e^[-（x-μ）^2/（2*σ^2）]

式中，e=欧拉数，π=π，σ=标准偏差，μ=平均值

这是我的代码：

> hist(iris$Sepal.Width[iris$Species == "setosa"], probability = TRUE,
   main = "Histogram of Sepal Width for Setosa Species Overlayed with Normal Distribution", 
   xlab = "Sepal Width (cm)", ylab = "Probability", cex.main = 0.9)

> sepal_width_setosa <- iris$Sepal.Width[iris$Species == "setosa"]

> sepal_width_setosa_mean <- mean(sepal_width_setosa)

> sepal_width_setosa_mean
[1] 3.428

> sepal_width_setosa_sd <- sd(sepal_width_setosa)

> sepal_width_setosa_sd
[1] 0.3790644

> sepal_width_setosa_variance = var(sepal_width_setosa)

> range(sepal_width_setosa)
[1] 2.3 4.4

> sepal_width_setosa_gaussian_distribution <- function(x){
  1 / (sepal_width_setosa_sd*sqrt(2*pi))*exp(-(x - sepal_width_setosa_mean)^2 / (2 * sepal_width_setosa_variance))
}


> curve(sepal_width_setosa_gaussian_distribution, from = 2.0, to = 4.5,
   col = "darkblue", lwd = 2, add = TRUE)

和正态性的统计检验

> library(nortest)
> ad.test(sepal_width_setosa)

安德森-达林正态性检验

数据：萼片\宽度\刚毛
A=0.49096，p值=0.2102

哇！p值大于0.05，所以我猜变量不是正态分布的

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问题：有没有一种方法可以把它放在一个循环中，得到所有鸢尾属物种的萼片宽度的直方图及其叠加的相关正态分布，然后并排显示图？如何将标准偏差标记1添加到3

为了回答您关于for循环和直方图的问题，我编写了一些我通常会做的示例代码

species_list <- split(iris, iris$Species) # split data.frame into lists by Species

par(mfrow = c(1,length(species_list))) # set the grid of the plot to be 1 row, 3 columns 
for(i in 1:length(species_list)){ # using length(species_list) for the # of species
  # subsetting lists with double brackets `[[`
  hist(species_list[[i]]$Sepal.Width, probability = T, 
       main = "", xlab = "Sepal Width (cm)", ylab = "Probability", # `main` is empty
       col = c("cyan","green3","yellow")[i]) # picks a new color each time (here out of 3)
  # my favorite function for plot titles
  mtext(toupper(names(species_list)[i]), # upper case species name for title
        side = 3, # 1 is bottom, 2 is left, 3 is top, 4 is right
        line = 0, # do not shift up or down
        font = 2) # 2 is bold
  # Simpler than building from scratch use the built in function:
  curve(dnorm(x, mean = mean(species_list[[i]]$Sepal.Width), 
                 sd = sd(species_list[[i]]$Sepal.Width)), 
                 yaxt = "n", add = TRUE, col = "darkblue", lwd = 2)
}
# At the end post a new title
mtext("Histograms of Sepal Width by Species Overlayed with a Normal Distribution", 
      outer = TRUE, # this puts it outside of the 3 plots
      side = 3, line = -1, # shift it down so we can see it
      font = 2)
par(mfrow = c(1,1)) # set the plot parameters back to a single plot when finished

---

species\u list为了回答你关于for循环和直方图的问题，我写了一些我通常会做的示例代码

species_list <- split(iris, iris$Species) # split data.frame into lists by Species

par(mfrow = c(1,length(species_list))) # set the grid of the plot to be 1 row, 3 columns 
for(i in 1:length(species_list)){ # using length(species_list) for the # of species
  # subsetting lists with double brackets `[[`
  hist(species_list[[i]]$Sepal.Width, probability = T, 
       main = "", xlab = "Sepal Width (cm)", ylab = "Probability", # `main` is empty
       col = c("cyan","green3","yellow")[i]) # picks a new color each time (here out of 3)
  # my favorite function for plot titles
  mtext(toupper(names(species_list)[i]), # upper case species name for title
        side = 3, # 1 is bottom, 2 is left, 3 is top, 4 is right
        line = 0, # do not shift up or down
        font = 2) # 2 is bold
  # Simpler than building from scratch use the built in function:
  curve(dnorm(x, mean = mean(species_list[[i]]$Sepal.Width), 
                 sd = sd(species_list[[i]]$Sepal.Width)), 
                 yaxt = "n", add = TRUE, col = "darkblue", lwd = 2)
}
# At the end post a new title
mtext("Histograms of Sepal Width by Species Overlayed with a Normal Distribution", 
      outer = TRUE, # this puts it outside of the 3 plots
      side = 3, line = -1, # shift it down so we can see it
      font = 2)
par(mfrow = c(1,1)) # set the plot parameters back to a single plot when finished

物种列表确定有一种方法。。。到目前为止你试过什么？您是否检查了
绘图
功能？您以前是否对
-循环使用过
。您可以使用
par
并排放置图形。或者使用
pdf
打印多页，等等。是的，我以前使用过循环，但在R中没有做过很多。我不太熟悉“par”的用法函数，谢谢你提到它。你似乎误解了p值。我对p值的理解有什么错？正态性AD检验的无效假设是：数据是正态分布的。替代假设：数据不是正态分布的。当p值为.21时，我们无法拒绝无效假设，并得出数据是正态分布的结论。。。到目前为止你试过什么？您是否检查了
绘图
功能？您以前是否对
-循环使用过
。您可以使用
par
并排放置图形。或者使用
pdf
打印多页，等等。是的，我以前使用过循环，但在R中没有做过很多。我不太熟悉“par”的用法函数，谢谢你提到它。你似乎误解了p值。我对p值的理解有什么错？正态性AD检验的无效假设是：数据是正态分布的。替代假设：数据不是正态分布的。当p值为.21时，我们无法拒绝无效假设，并得出数据为正态分布的结论。