r语言 - 忠实数据集的密度列与MASS中ken2d函数计算密度存在差异的原因

Question

我尝试在ggplot2提供的faithfuld数据集中计算密度列，发现计算结果与在MASS中使用ken2d函数不一样，我没有找到faithfuld中如何计算的信息，你能解释一下为什么会发生这种情况吗?

library(ggplot2)
library(MASS)
attach(faithfuld)
head(faithfuld)
#> # A tibble: 6 × 3
#>   eruptions waiting density
#>       <dbl>   <dbl>   <dbl>
#> 1      1.6       43 0.00322
#> 2      1.65      43 0.00384
#> 3      1.69      43 0.00444
#> 4      1.74      43 0.00498
#> 5      1.79      43 0.00542
#> 6      1.84      43 0.00574
kde2d(eruptions, waiting)$z
#>              [,1]        [,2]        [,3]        [,4]        [,5]        [,6]
#>  [1,] 0.001581071 0.002335108 0.002734390 0.002856616 0.002878211 0.002880409
#>  [2,] 0.002335108 0.003448757 0.004038463 0.004218981 0.004250875 0.004254120
#>  [3,] 0.002734390 0.004038463 0.004729004 0.004940389 0.004977736 0.004981536
#>  [4,] 0.002856616 0.004218981 0.004940389 0.005161222 0.005200239 0.005204209
#>  [5,] 0.002878211 0.004250875 0.004977736 0.005200239 0.005239550 0.005243551
#>  [6,] 0.002880409 0.004254120 0.004981536 0.005204209 0.005243551 0.005247554
#>  [7,] 0.002880537 0.004254310 0.004981759 0.005204441 0.005243784 0.005247788
#>  [8,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#>  [9,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [10,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [11,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [12,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [13,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [14,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [15,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [16,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [17,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [18,] 0.002880541 0.004254316 0.004981766 0.005204449 0.005243792 0.005247796
#> [19,] 0.002880537 0.004254310 0.004981759 0.005204441 0.005243784 0.005247788
#> [20,] 0.002880409 0.004254120 0.004981536 0.005204209 0.005243551 0.005247554
#> [21,] 0.002878211 0.004250875 0.004977736 0.005200239 0.005239550 0.005243551
#> [22,] 0.002856616 0.004218981 0.004940389 0.005161222 0.005200239 0.005204209
#> [23,] 0.002734390 0.004038463 0.004729004 0.004940389 0.004977736 0.004981536
#> [24,] 0.002335108 0.003448757 0.004038463 0.004218981 0.004250875 0.004254120
#> [25,] 0.001581071 0.002335108 0.002734390 0.002856616 0.002878211 0.002880409
#>              [,7]        [,8]        [,9]       [,10]       [,11]       [,12]
#>  [1,] 0.002880537 0.002880541 0.002880541 0.002880541 0.002880541 0.002880541
#>  [2,] 0.004254310 0.004254316 0.004254316 0.004254316 0.004254316 0.004254316
#>  [3,] 0.004981759 0.004981766 0.004981766 0.004981766 0.004981766 0.004981766
#>  [4,] 0.005204441 0.005204449 0.005204449 0.005204449 0.005204449 0.005204449
#>  [5,] 0.005243784 0.005243792 0.005243792 0.005243792 0.005243792 0.005243792
#>  [6,] 0.005247788 0.005247796 0.005247796 0.005247796 0.005247796 0.005247796
#>  [7,] 0.005248022 0.005248030 0.005248030 0.005248030 0.005248030 0.005248030
#>  [8,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#>  [9,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [10,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [11,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [12,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [13,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [14,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [15,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [16,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [17,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [18,] 0.005248030 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [19,] 0.005248022 0.005248030 0.005248030 0.005248030 0.005248030 0.005248030
#> [20,] 0.005247788 0.005247796 0.005247796 0.005247796 0.005247796 0.005247796
#> [21,] 0.005243784 0.005243792 0.005243792 0.005243792 0.005243792 0.005243792
#> [22,] 0.005204441 0.005204449 0.005204449 0.005204449 0.005204449 0.005204449
#> [23,] 0.004981759 0.004981766 0.004981766 0.004981766 0.004981766 0.004981766
#> [24,] 0.004254310 0.004254316 0.004254316 0.004254316 0.004254316 0.004254316
#> [25,] 0.002880537 0.002880541 0.002880541 0.002880541 0.002880541 0.002880541
#>             [,13]       [,14]       [,15]       [,16]       [,17]       [,18]
#>  [1,] 0.002880541 0.002880541 0.002880541 0.002880541 0.002880541 0.002880541
#>  [2,] 0.004254316 0.004254316 0.004254316 0.004254316 0.004254316 0.004254316
#>  [3,] 0.004981766 0.004981766 0.004981766 0.004981766 0.004981766 0.004981766
#>  [4,] 0.005204449 0.005204449 0.005204449 0.005204449 0.005204449 0.005204449
#>  [5,] 0.005243792 0.005243792 0.005243792 0.005243792 0.005243792 0.005243792
#>  [6,] 0.005247796 0.005247796 0.005247796 0.005247796 0.005247796 0.005247796
#>  [7,] 0.005248030 0.005248030 0.005248030 0.005248030 0.005248030 0.005248030
#>  [8,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#>  [9,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [10,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [11,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [12,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [13,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [14,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [15,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [16,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [17,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [18,] 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038 0.005248038
#> [19,] 0.005248030 0.005248030 0.005248030 0.005248030 0.005248030 0.005248030
#> [20,] 0.005247796 0.005247796 0.005247796 0.005247796 0.005247796 0.005247796
#> [21,] 0.005243792 0.005243792 0.005243792 0.005243792 0.005243792 0.005243792
#> [22,] 0.005204449 0.005204449 0.005204449 0.005204449 0.005204449 0.005204449
#> [23,] 0.004981766 0.004981766 0.004981766 0.004981766 0.004981766 0.004981766
#> [24,] 0.004254316 0.004254316 0.004254316 0.004254316 0.004254316 0.004254316
#> [25,] 0.002880541 0.002880541 0.002880541 0.002880541 0.002880541 0.002880541
#>             [,19]       [,20]       [,21]       [,22]       [,23]       [,24]
#>  [1,] 0.002880537 0.002880409 0.002878211 0.002856616 0.002734390 0.002335108
#>  [2,] 0.004254310 0.004254120 0.004250875 0.004218981 0.004038463 0.003448757
#>  [3,] 0.004981759 0.004981536 0.004977736 0.004940389 0.004729004 0.004038463
#>  [4,] 0.005204441 0.005204209 0.005200239 0.005161222 0.004940389 0.004218981
#>  [5,] 0.005243784 0.005243551 0.005239550 0.005200239 0.004977736 0.004250875
#>  [6,] 0.005247788 0.005247554 0.005243551 0.005204209 0.004981536 0.004254120
#>  [7,] 0.005248022 0.005247788 0.005243784 0.005204441 0.004981759 0.004254310
#>  [8,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#>  [9,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [10,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [11,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [12,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [13,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [14,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [15,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [16,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [17,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [18,] 0.005248030 0.005247796 0.005243792 0.005204449 0.004981766 0.004254316
#> [19,] 0.005248022 0.005247788 0.005243784 0.005204441 0.004981759 0.004254310
#> [20,] 0.005247788 0.005247554 0.005243551 0.005204209 0.004981536 0.004254120
#> [21,] 0.005243784 0.005243551 0.005239550 0.005200239 0.004977736 0.004250875
#> [22,] 0.005204441 0.005204209 0.005200239 0.005161222 0.004940389 0.004218981
#> [23,] 0.004981759 0.004981536 0.004977736 0.004940389 0.004729004 0.004038463
#> [24,] 0.004254310 0.004254120 0.004250875 0.004218981 0.004038463 0.003448757
#> [25,] 0.002880537 0.002880409 0.002878211 0.002856616 0.002734390 0.002335108
#>             [,25]
#>  [1,] 0.001581071
#>  [2,] 0.002335108
#>  [3,] 0.002734390
#>  [4,] 0.002856616
#>  [5,] 0.002878211
#>  [6,] 0.002880409
#>  [7,] 0.002880537
#>  [8,] 0.002880541
#>  [9,] 0.002880541
#> [10,] 0.002880541
#> [11,] 0.002880541
#> [12,] 0.002880541
#> [13,] 0.002880541
#> [14,] 0.002880541
#> [15,] 0.002880541
#> [16,] 0.002880541
#> [17,] 0.002880541
#> [18,] 0.002880541
#> [19,] 0.002880537
#> [20,] 0.002880409
#> [21,] 0.002878211
#> [22,] 0.002856616
#> [23,] 0.002734390
#> [24,] 0.002335108
#> [25,] 0.001581071

^{创建于2023-04-03 with reprex v2.0.2}

Answer 1

ggplot2提供的faithfuld数据集计算如下:

MASS::kde2d(faithful$eruptions, faithful$waiting, h = c(1, 10), n = 75)

你可以在GitHub页面找到这些信息:https://github.com/tidyverse/ggplot2/blob/main/data-raw/faithfuld.R