@joran 的回复/评论让我思考适当的缩放因子是什么。为了后代的利益,这是结果。
当纵轴为频率(又名计数)时
因此,以 bin 计数测量的垂直轴的缩放因子为
在这种情况下,与N = 164
bin 宽度为0.1
,平滑线中 y 的美感应该是:
y = ..density..*(164 * 0.1)
因此,以下代码生成一条针对以频率(又称计数)测量的直方图缩放的“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(x = v)) +
geom_histogram(aes(y = ..count..), breaks = b1,
fill = "blue", color = "black") +
geom_density(aes(y = ..density..*(164*0.1)))
hist.1a
当纵轴为相对频率时
利用上面的内容,我们可以写出
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
当纵轴为密度时
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