使用 dplyr 生成多個成對比較

Question

我想使用 dplyr 將數據集拆分為多個變量，然后自動在特定變量的不同級別之間進行成對比較。

為了說明這個概念，我生成了一些模擬數據

df <- data.frame(id = rep(c(1,2,3),each=4),trt = rep(c("a","b"),each=3),method=rep(c("m1","m2","m3")),result=rnorm(12))

> df
   id trt method       result
1   1   a     m1 -1.417685956
2   1   a     m2  0.007361868
3   1   a     m3 -0.763148987
4   1   b     m1 -0.203534562
5   2   b     m2 -0.359201092
6   2   b     m3 -1.256859805
7   2   a     m1 -0.160111516
8   2   a     m2 -1.833714579
9   3   a     m3  0.423011535
10  3   b     m1 -0.382595107
11  3   b     m2 -0.880756365
12  3   b     m3 -0.447458763

對於每個 id 和處理，我想在每個方法的結果之間進行成對比較。 在我的情況下，成對比較是結果的簡單划分。 也就是說，我想生成 9 個可能的分區 m1/m1、m1/m2、m1/m3、m2/m1、...、m3/m3。 這意味着每種方法都充當參考和比較器。

我正在考慮開始使用傳播

spread(df,method,result)

這會給我

  id trt           m1         m2         m3
1  1   a  0.541351984 -0.1223797 -1.0296924
2  1   b -1.860059387 -0.1117496 -0.4026570
3  2   a -0.692783955  2.1372636  1.6603239
4  2   b -0.006360277 -0.1880784  1.4650995
5  3   a  1.475971742  1.1721263 -0.4858894
6  3   b  2.149726008 -0.6282801 -0.7653839

但是，當它們現在是不同的列時，如何自動生成所有可能的比較（在我的情況下是除法）？ 最后，我想在這些成對比較中計算所有受試者和治療的匯總統計數據。

感謝您對此的任何意見

Answer 1

你不需要傳播。

dplyr：

inner_join(df,df,by = c("trt"="trt","id"="id")) %>% filter(method.x>=method.y) %>%
                                                 mutate(div = result.x/result.y)

   id trt method.x   result.x method.y   result.y         div
1   1   a       m1  1.8453179       m1  1.8453179   1.0000000
2   1   a       m2  0.5514105       m1  1.8453179   0.2988160
3   1   a       m2  0.5514105       m2  0.5514105   1.0000000
4   1   a       m3 -0.9281152       m1  1.8453179  -0.5029568
5   1   a       m3 -0.9281152       m2  0.5514105  -1.6831657
6   1   a       m3 -0.9281152       m3 -0.9281152   1.0000000
7   1   b       m1 -0.8557873       m1 -0.8557873   1.0000000

data.table：

library(data.table)
setDT(df)
df[df,on=.(trt,id),allow.cartesian=T][method>=i.method,.(trt,id,method,i.method,div = i.result/result)]

Answer 2

我不是 100% 確定您想要的 output 會是什么樣子，但這里有一個dplyr方法：

library(dplyr)

df <- data.frame(id = rep(c(1,2,3),each=4),trt = rep(c("a","b"),each=3),method=rep(c("m1","m2","m3")),result=rnorm(12))

df %>% 
  inner_join(df, by = c("id", "trt")) %>% 
  mutate(comparison = result.x / result.y) 

#>    id trt method.x    result.x method.y    result.y comparison
#> 1   1   a       m1 -0.21178736       m1 -0.21178736   1.0000000
#> 2   1   a       m1 -0.21178736       m2  0.85283041  -0.2483347
#> 3   1   a       m1 -0.21178736       m3  1.20428478  -0.1758615
#> 4   1   a       m2  0.85283041       m1 -0.21178736  -4.0268240
#> 5   1   a       m2  0.85283041       m2  0.85283041   1.0000000
#> 6   1   a       m2  0.85283041       m3  1.20428478   0.7081634
#> 7   1   a       m3  1.20428478       m1 -0.21178736  -5.6862921
#> 8   1   a       m3  1.20428478       m2  0.85283041   1.4121035
#> 9   1   a       m3  1.20428478       m3  1.20428478   1.0000000
#> 10  1   b       m1  0.01242714       m1  0.01242714   1.0000000
#> 11  2   b       m2 -0.11301668       m2 -0.11301668   1.0000000
#> 12  2   b       m2 -0.11301668       m3 -0.38098395   0.2966442
#> 13  2   b       m3 -0.38098395       m2 -0.11301668   3.3710417
#> 14  2   b       m3 -0.38098395       m3 -0.38098395   1.0000000
#> 15  2   a       m1  1.04499143       m1  1.04499143   1.0000000
#> 16  2   a       m1  1.04499143       m2  0.90353396   1.1565602
#> 17  2   a       m2  0.90353396       m1  1.04499143   0.8646329
#> 18  2   a       m2  0.90353396       m2  0.90353396   1.0000000
#> 19  3   a       m3  0.12398337       m3  0.12398337   1.0000000
#> 20  3   b       m1  1.22590343       m1  1.22590343   1.0000000
#> 21  3   b       m1  1.22590343       m2 -1.37266240  -0.8930844
#> 22  3   b       m1  1.22590343       m3  0.41983609   2.9199572
#> 23  3   b       m2 -1.37266240       m1  1.22590343  -1.1197150
#> 24  3   b       m2 -1.37266240       m2 -1.37266240   1.0000000
#> 25  3   b       m2 -1.37266240       m3  0.41983609  -3.2695198
#> 26  3   b       m3  0.41983609       m1  1.22590343   0.3424708
#> 27  3   b       m3  0.41983609       m2 -1.37266240  -0.3058553
#> 28  3   b       m3  0.41983609       m3  0.41983609   1.0000000

^{由代表 package (v0.3.0) 於 2020 年 6 月 14 日創建}

Answer 3

分組后使用combn

library(dplyr)
library(tidyr)
df %>% 
   group_by(id, trt) %>% 
   summarise(result = if(n() >=2) list(combn(result, 2, FUN = function(x) x[1]/x[2])) else list(result)) %>% 
   unnest(c(result))

對於完整組合，請使用outer

df %>% 
    group_by(id, trt) %>% 
    summarise(result = list(c(outer(result, result, FUN = `/`)))) %>% 
    unnest(c(result))
# A tibble: 28 x 3
# Groups:   id [3]
#      id trt   result
#   <dbl> <chr>  <dbl>
# 1     1 a      1    
# 2     1 a      0.597
# 3     1 a     -0.121
# 4     1 a      1.68 
# 5     1 a      1    
# 6     1 a     -0.202
# 7     1 a     -8.29 
# 8     1 a     -4.95 
# 9     1 a      1    
#10     1 b      1    
# … with 18 more rows

---

或merge

transform(merge(df, df, by = c('trt', 'id')), ratio = result.x/result.y)
#    trt id method.x    result.x method.y    result.y      ratio
#1    a  1       m1  2.19881035       m1  2.19881035  1.0000000
#2    a  1       m1  2.19881035       m2  1.31241298  1.6753952
#3    a  1       m1  2.19881035       m3 -0.26514506 -8.2928582
#4    a  1       m2  1.31241298       m1  2.19881035  0.5968741
#5    a  1       m2  1.31241298       m2  1.31241298  1.0000000
#6    a  1       m2  1.31241298       m3 -0.26514506 -4.9497924
#7    a  1       m3 -0.26514506       m1  2.19881035 -0.1205857
#8    a  1       m3 -0.26514506       m2  1.31241298 -0.2020287
#9    a  1       m3 -0.26514506       m3 -0.26514506  1.0000000
#10   a  2       m1 -0.78860284       m1 -0.78860284  1.0000000
#11   a  2       m1 -0.78860284       m2 -0.59461727  1.3262360
#12   a  2       m2 -0.59461727       m1 -0.78860284  0.7540136
#13   a  2       m2 -0.59461727       m2 -0.59461727  1.0000000
#14   a  3       m3  1.65090747       m3  1.65090747  1.0000000
#15   b  1       m1  0.54319406       m1  0.54319406  1.0000000
#16   b  2       m2 -0.41433995       m2 -0.41433995  1.0000000
#17   b  2       m2 -0.41433995       m3 -0.47624689  0.8700108
#18   b  2       m3 -0.47624689       m2 -0.41433995  1.1494110
#19   b  2       m3 -0.47624689       m3 -0.47624689  1.0000000
#20   b  3       m1 -0.05402813       m1 -0.05402813  1.0000000
#21   b  3       m1 -0.05402813       m2  0.11924524 -0.4530841
#22   b  3       m1 -0.05402813       m3  0.24368743 -0.2217108
#23   b  3       m2  0.11924524       m1 -0.05402813 -2.2070956
#24   b  3       m2  0.11924524       m2  0.11924524  1.0000000
#25   b  3       m2  0.11924524       m3  0.24368743  0.4893368
#26   b  3       m3  0.24368743       m1 -0.05402813 -4.5103810
#27   b  3       m3  0.24368743       m2  0.11924524  2.0435821
#28   b  3       m3  0.24368743       m3  0.24368743  1.0000000