TukeyHSD和multcompView與dplyr和掃帚在R

它在broom網站中提到它可用於TukeyHSD和multcomp（請參閱here）。然而，我不知道如何使用broom代替TukeyHSD和multcomp。TukeyHSD和multcompView與dplyr和掃帚在R

參見下面給出的MWE。

df1 <- data.frame(
    Rep = factor(rep(1:3, each = 4, times = 2)), 
    Trt = rep(paste0("T", 1:4), times = 6), 
    Loc = rep(paste0("Loc", 1:2), each = 12), 
    Y = rnorm(24) 
) 

library(dplyr) 
df2 <- filter(df1, Loc=="Loc1") 

fm1 <- aov(Y ~ Rep + Trt , data = df2) 
anova(fm1) 

library(multcompView) 

fm1Tukey1 <- 
    data.frame(Letter = multcompLetters(TukeyHSD(fm1)$Trt[, "p adj"])$Letters) 
fm1Tukey <- data.frame(Trt = row.names(fm1Tukey1), fm1Tukey1) 

fm1Means1 <- 
    data.frame(
     Mean = as.matrix(model.tables(x = fm1, type = "means")[[1]]$Trt) 
    , SE = model.tables(x = fm1, type = "means", se = TRUE)$se$Trt 
) 
names(fm1Means1) <- c("Mean", "SE") 

fm1Means2 <- data.frame(Trt = row.names(fm1Means1), fm1Means1) 
fm1Means <- left_join(fm1Means2, fm1Tukey) 


library(dplyr) 
fm3 <- 
    df1 %>% 
    group_by(Loc) %>% 
    do(model = aov(Y ~ Rep + Trt , data = .)) 

fm3$model 

library(broom) 

fm3 %>% tidy(model)

來源

2017-09-23 MYaseen208

這個解決方案呢？

fm3 <- 
    df1 %>% 
    group_by(Loc) %>% 
    do(multitst = TukeyHSD(aov(Y ~ Rep + Trt , data = .))) 
fm3 %>% tidy(multitst)

結果是：

# A tibble: 18 x 7 
# Groups: Loc [2] 
     Loc term comparison estimate conf.low conf.high adj.p.value 
    <fctr> <fctr>  <chr>  <dbl>  <dbl>  <dbl>  <dbl> 
1 Loc1 Rep  2-1 1.06654704 -0.5666584 2.6997525 0.1920531 
2 Loc1 Rep  3-1 0.07349636 -1.5597091 1.7067018 0.9895627 
3 Loc1 Rep  3-2 -0.99305068 -2.6262561 0.6401548 0.2283849 
4 Loc1 Trt  T2-T1 0.66688371 -1.4607989 2.7945663 0.7105928 
5 Loc1 Trt  T3-T1 -0.34873829 -2.4764209 1.7789443 0.9382673 
6 Loc1 Trt  T4-T1 0.76089899 -1.3667836 2.8885816 0.6281933 
7 Loc1 Trt  T3-T2 -1.01562201 -3.1433046 1.1120606 0.4201776 
8 Loc1 Trt  T4-T2 0.09401528 -2.0336673 2.2216979 0.9985800 
9 Loc1 Trt  T4-T3 1.10963728 -1.0180453 3.2373199 0.3556331 
10 Loc2 Rep  2-1 -0.59970808 -2.4360070 1.2365908 0.6023328 
11 Loc2 Rep  3-1 -0.29558179 -2.1318807 1.5407171 0.8768041 
12 Loc2 Rep  3-2 0.30412629 -1.5321726 2.1404252 0.8702266 
13 Loc2 Trt  T2-T1 -1.06715766 -3.4594233 1.3251080 0.4703902 
14 Loc2 Trt  T3-T1 -1.38659230 -3.7788579 1.0056733 0.2828393 
15 Loc2 Trt  T4-T1 -1.23727832 -3.6295439 1.1549873 0.3616019 
16 Loc2 Trt  T3-T2 -0.31943464 -2.7117003 2.0728310 0.9646736 
17 Loc2 Trt  T4-T2 -0.17012066 -2.5623863 2.2221450 0.9942021 
18 Loc2 Trt  T4-T3 0.14931398 -2.2429516 2.5415796 0.9960495

來源

2017-09-23 10:26:32

TukeyHSD和multcompView與dplyr和掃帚在R

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