"密度"曲线叠加在直方图上，其中纵轴是频率（又名计数）还是相对频率？ [英] "Density" curve overlay on histogram where vertical axis is frequency (aka count) or relative frequency?

问题描述

当垂直轴是频率或相对频率时，是否有一种方法可以将类似于密度曲线的东西重叠？ （不是一个实际的密度函数，因为该区域不需要集成到1）。下面的问题是类似的：
ggplot2：具有正常曲线的直方图，并且用户自行回答该想法以缩放 .. count .. 在 geom_density（）。然而这看起来不寻常。

以下代码会产生一个过度膨胀的密度线。

  df1 < -  data.frame（v = rnorm（164，mean = 9，sd = 1.5））
 b1 < -  seq（4.5,12，by = 0.1）
 hist.1a< -  ggplot（df1，aes（v））+ 
 stat_bin（aes（y = ..count ..），color =black，fill =blue，
 breaks = b1）+ 
 geom_density（aes（y = ..count ..））
 hist.1a 
  

解决方案

@ joran的回应/评论让我想到了适当的缩放因子是什么。为了后代的缘故，结果如下。

垂直轴为频率（又名计数）

因此，缩放以bin计数衡量的垂直轴的因子是bb
$ b

在这种情况下，使用 N = 164 和垃圾箱宽度为 0.1 ，平滑线中的y的美学应该是：

  y = ..density .. *（164 * 0.1）
  

因此，下面的代码产生一个密度 （aka计数）。

  df1 < -  data.frame（v = rnorm（164，mean = 9，sd = 1.5））
 b1 < -  seq（4.5,12，by = 0.1）
 hist.1a < -  ggplot（df1，aes（x = v））+ 
 geom_histogram（ aes（y = ..count ..），休息= b1，
填充=蓝色，颜色=黑色）+ 
 geom_density（aes（y = ..density .. *（164 * 0.1）））
 hist.1a 
  

纵轴为相对频率 /i.stack.imgur.com/VL0LP.gifalt =freq>

使用上面的代码，我们可以写出

  hist.1b < -  ggplot（df1，aes（x = v））+ 
 geom_histogram（aes（y = ..count ../ 164 ），break = b1，
 fill =blue，color =black）+ 
 geom_density（aes（y = ..density .. *（0.1）））
 hist。 1b 
  

<$ p $

p> hist.1c < - ggplot（df1，aes（x = v））+
geom_histogram（aes（y = ..density ..），breaks = b1，
fill =blue，color =black）+
geom_density（aes（y = ..density ..） ）
hist.1c


Is there a method to overlay something analogous to a density curve when the vertical axis is frequency or relative frequency? (Not an actual density function, since the area need not integrate to 1.) The following question is similar: ggplot2: histogram with normal curve, and the user self-answers with the idea to scale ..count.. inside of geom_density(). However this seems unusual.

The following code produces an overinflated "density" line.

df1            <- data.frame(v = rnorm(164, mean = 9, sd = 1.5))
b1             <- seq(4.5, 12, by = 0.1)
hist.1a        <- ggplot(df1, aes(v)) + 
                    stat_bin(aes(y = ..count..), color = "black", fill = "blue",
                             breaks = b1) + 
                    geom_density(aes(y = ..count..))
hist.1a

解决方案

@joran's response/comment got me thinking about what the appropriate scaling factor would be. For posterity's sake, here's the result.

When Vertical Axis is Frequency (aka Count)

Thus, the scaling factor for a vertical axis measured in bin counts is

In this case, with N = 164 and the bin width as 0.1, the aesthetic for y in the smoothed line should be:

y = ..density..*(164 * 0.1)

Thus the following code produces a "density" line scaled for a histogram measured in frequency (aka count).

df1            <- data.frame(v = rnorm(164, mean = 9, sd = 1.5))
b1             <- seq(4.5, 12, by = 0.1)
hist.1a        <- ggplot(df1, aes(x = v)) + 
                    geom_histogram(aes(y = ..count..), breaks = b1, 
                                   fill = "blue", color = "black") + 
                    geom_density(aes(y = ..density..*(164*0.1)))
hist.1a

When Vertical Axis is Relative Frequency

Using the above, we could write

hist.1b        <- ggplot(df1, aes(x = v)) + 
                    geom_histogram(aes(y = ..count../164), breaks = b1, 
                                   fill = "blue", color = "black") + 
                    geom_density(aes(y = ..density..*(0.1)))
hist.1b

When Vertical Axis is Density

hist.1c        <- ggplot(df1, aes(x = v)) + 
                    geom_histogram(aes(y = ..density..), breaks = b1, 
                                   fill = "blue", color = "black") + 
                    geom_density(aes(y = ..density..))
hist.1c

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