TukeyHSD und multcompView mit dplyr und Besen in der R

Question

Es in broom Website erwähnt wird, dass es für TukeyHSD und multcomp verwendet werden kann (here sehen). Jedoch konnte ich nicht herausfinden, wie man broom für und multcomp verwendet.

Siehe MWE unten gegeben.

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) 

Answer 1

Was ist mit dieser Lösung?

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

Das Ergebnis ist:

# 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