Question

我有一个包含多个变量的大型数据集。我需要进行双向方差分析，然后使用Tukey HSD进行事后成对的多重比较。

我的前25个条目的数据头是这样的：

> head(my_data2, 25 )
   CellType variable     value
1     Cell1       W1 18.780294
2     Cell1       W1 13.932397
3     Cell1       W1 20.877093
4     Cell1       W1  9.291295
5     Cell1       W1 10.939570
6     Cell1       W1 12.236713
7     Cell1       W1 13.810722
8     Cell1       W1 23.944473
9     Cell1       W1 17.355429
10    Cell1       W1 18.248215
11    Cell2       W1 17.988200
12    Cell2       W1 15.427909
13    Cell2       W1 21.839687
14    Cell2       W1 22.322325
15    Cell2       W1 12.535762
16    Cell2       W1 12.743278
17    Cell2       W1 15.007214
18    Cell2       W1 12.054787
19    Cell2       W1 15.639977
20    Cell2       W1 16.006960
21    Cell3       W1 17.452199
22    Cell3       W1 23.280391
23    Cell3       W1  7.902728
24    Cell3       W1  8.353992
25    Cell3       W1 24.360250

我进行方差分析

#ANOVA
my_data2$CellType <- as.factor(my_data2$CellType)
my_ANOVA = aov(value ~ CellType + variable + CellType:variable, data = my_data2)
summary(my_ANOVA)

然后临时

my_posthoc =TukeyHSD(my_ANOVA, which = "CellType:variable") 
my_posthoc

到目前为止，一切都还可以，但是我posthoc的输出包括所有成对比较，这给了我们超过2200行很大的投入。例如我的输出是这样的：

> my_posthoc
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = value ~ CellType + variable + CellType:variable, data = my_data2)

$`CellType:variable`
                       diff          lwr         upr     p adj
Cell2:W1-Cell1:W1   0.21499 -29.46177884  29.8917588 1.0000000
Cell3:W1-Cell1:W1   0.88234 -28.79442884  30.5591088 1.0000000
Cell4:W1-Cell1:W1   1.24301 -28.43375884  30.9197788 1.0000000
Cell5:W1-Cell1:W1   1.61684 -28.05992884  31.2936088 1.0000000
Cell6:W1-Cell1:W1   0.65009 -29.02667884  30.3268588 1.0000000
Cell7:W1-Cell1:W1   1.08223 -28.59453884  30.7589988 1.0000000
Cell1:W2-Cell1:W1   9.00094 -20.67582884  38.6777088 1.0000000
Cell2:W2-Cell1:W1  27.62765  -2.04911884  57.3044188 0.1249342
Cell3:W2-Cell1:W1  29.40077  -0.27599884  59.0775388 0.0570151
Cell4:W2-Cell1:W1  28.84731  -0.82945884  58.5240788 0.0736530
Cell5:W2-Cell1:W1  42.51407  12.83730116  72.1908388 0.0000144
Cell6:W2-Cell1:W1  30.78610   1.10933116  60.4628688 0.0288235
Cell7:W2-Cell1:W1  27.62966  -2.04710884  57.3064288 0.1248307
Cell1:W3-Cell1:W1  20.95847  -8.71829884  50.6352388 0.7816085
Cell2:W3-Cell1:W1  42.50116  12.82439116  72.1779288 0.0000146
Cell3:W3-Cell1:W1  47.07037  17.39360116  76.7471388 0.0000004
Cell4:W3-Cell1:W1  47.26760  17.59083116  76.9443688 0.0000003
Cell5:W3-Cell1:W1  64.08026  34.40349116  93.7570288 0.0000000
Cell6:W3-Cell1:W1  53.90284  24.22607116  83.5796088 0.0000000

最后说：

[ reached getOption("max.print") -- omitted 2290 rows ]

但是，我只对每个变量内的比较感兴趣，而对它们之间的比较不感兴趣。以上述输出为例，我只需要 Cell1:W1-Cell2:W1。全部都在同一个变量w1中。或例如Cell6:W3-Cell1:W3。我对Cell6:W3-Cell6:W1

不感兴趣

如何指定？ 谢谢

Answer 1

我采取了简单的诚实方法，将术语（行名）分为四个部分并进行了过滤。

library(dplyr); library(tibble); library(purrr)  # OR library(tidyverse)  # EDITED

my_posthoc2 <- my_posthoc %>% 
  pluck("CellType:variablen") %>%             # get element of list
  as_tibble(rownames = "Term") %>%            # convert to tibble
  separate(Term,                              # separate terms by - and :
           into = c("LL", "LR", "RL", "RR"), 
           sep = "-|:", 
           remove = FALSE) 

my_posthoc2 %>% 
  filter(LR == "W1", RR == "W1")  # get Cell1:W1-Cell2:W1

Answer 2

由于您指定了“我只对每个变量内的比较感兴趣，而对它们之间的比较不感兴趣”，因此您无需包括交互项CellType:variable

您可以将模型重写为：

my_ANOVA = aov(value ~ CellType + variable, data = my_data2)

R中的双向ANOVA选择Tukey HSD的输出

2 个答案: