計算R中離散資料的密度

我想用離散資料在 R 中繪制密度直方圖。鑒于我的資料集非常大，我需要在繪圖之前計算密度。

但是，我發現與instats::density相比，使用該函式提供了不同的結果。這是為什么？ggplot2 的 ..density.. 結果也符合預期，而 stats::density 則不然。..density..ggplot2

請參閱下面的可重現示例。

非常感謝

library(tidyverse)
library(patchwork)

df <- data.frame(A = round(rnorm(1000)),
           B = round(rnorm(1000)),
           C = round(rnorm(1000))) %>% 
  pivot_longer(cols = everything(), names_to = "group")

dens_df <- df %>% 
  group_by(group) %>% 
  summarise(dens = list(density(value, from = -3, to = 6, n = length(-3:6)))) %>% #compute density and nest into list
  mutate(density.x = map(dens, ~.x[["x"]]), #extract x values
         density.y = map(dens, ~.x[["y"]])) %>%  #extract y values
  select(-dens) %>% 
  unnest(cols = c(density.x, density.y))

plot_dens <- dens_df %>% 
  ggplot() 
  aes(x = density.x, y = density.y) %>% 
  geom_col() 
  scale_x_continuous(breaks = seq(-3,10,1)) 
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), geom = "point", col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), geom = "point", size = 2, col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), geom = "line", col = "red")  
  facet_wrap(~group) 
  labs(title = "using stats::density")

plot_geom <- df %>% 
  ggplot() 
  aes(x = value, y = ..density..) %>% 
  geom_histogram(
    binwidth = 1,
    col = "white") 
  scale_x_continuous(breaks = seq(-3,10,1)) 
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), geom = "point", col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), geom = "point", size = 2, col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), geom = "line", col = "red")  
  facet_wrap(~group) 
  labs(title = "using ggplot2 ..density..")


plot_dens   plot_geom

uj5u.com熱心網友回復：

簡單的答案是，這stats::density不是用于離散資料的正確函式。stats::density使用平滑內核并提供表示 -3 到 6 之間的連續密度的曲線，即使僅在 10 個點采樣。這與離散資料的密度不同，后者只是每個 bin 的頻率除以觀測值的數量。這就是geom_histogram繪制的內容。

獲得離散密度的計算有效方法是使用hist甚至只是table(x)/length(x).

這是一個使用預先計算的密度的示例hist

dens_df <- df %>% 
  group_by(group) %>% 
  summarise(dens = list(hist(value, breaks = seq(-3.5, 6.5), plot = FALSE))) %>% 
  mutate(density.x = map(dens, ~.x[["mids"]]), #extract x values
         density.y = map(dens, ~.x[["density"]])) %>%  #extract y values
  select(-dens) %>% 
  unnest(cols = c(density.x, density.y))

我們可以看到這導致與 相同的圖geom_histogram，使用您自己的繪圖代碼，但使用更改后的dens_df

plot_dens <- dens_df %>% 
  ggplot() 
  aes(x = density.x, y = density.y) %>% 
  geom_col() 
  scale_x_continuous(breaks = seq(-3,10,1)) 
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1),
                geom = "point", col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), 
                geom = "point", size = 2, col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), 
                geom = "line", col = "red")  
  facet_wrap(~group) 
  labs(title = "using graphics::hist")

plot_geom <- df %>% 
  ggplot() 
  aes(x = value, y = ..density..) %>% 
  geom_histogram(
    binwidth = 1,
    col = "white") 
  scale_x_continuous(breaks = seq(-3,10,1)) 
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), 
                geom = "point", col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), 
                geom = "point", size = 2, col = "red")  
  stat_function(fun = dnorm, n = 10, args = list(mean = 0, sd = 1), 
                geom = "line", col = "red")  
  facet_wrap(~group) 
  labs(title = "using ggplot2 ..density..")

plot_dens   plot_geom

標籤：r ggplot2 核密度 密度图

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