0
chocolate <- data.frame(
Sabor =
c(5, 7, 3,
4, 2, 6,
5, 3, 6,
5, 6, 0,
7, 4, 0,
7, 7, 0,
6, 6, 0,
4, 6, 1,
6, 4, 0,
7, 7, 0,
2, 4, 0,
5, 7, 4,
7, 5, 0,
4, 5, 0,
6, 6, 3
),
Tipo = factor(rep(c("A", "B", "C"), 15)),
Provador = factor(rep(1:15, rep(3, 15))))
tapply(chocolate$Sabor, chocolate$Tipo, mean)
ajuste <- lm(chocolate$Sabor ~ chocolate$Tipo + chocolate$Provador)
summary(ajuste)
anova(ajuste)
a1 <- aov(chocolate$Sabor ~ chocolate$Tipo + chocolate$Provador)
posthoc <- TukeyHSD(x=a1, 'chocolate$Tipo', conf.level=0.95)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = chocolate$Sabor ~ chocolate$Tipo + chocolate$Provador)
$`chocolate$Tipo`
diff lwr upr p adj
B-A -0.06666667 -1.803101 1.669768 0.9950379
C-A -3.80000000 -5.536435 -2.063565 0.0000260
C-B -3.73333333 -5.469768 -1.996899 0.0000337
以下是一些使用TukeyHSD
的示例代碼。輸出是一個矩陣,我希望這些值以科學記數法顯示。我試過使用scipen
和設置options(digits = 20)
但我的實際數據中的一些值仍然太小,因此p adj值爲0.00000000000000000000R:顯示科學記數法
如何獲取以科學記數法顯示的值?
'LM(薩波〜TIPO + Provador ,數據=巧克力)'還有'aov(...,data = ...)'更短。順便說一句:你的阿爾法(顯着性水平)是什麼? – jogo
我沒有指定alpha,所以我認爲它是默認值(0.05?) – Adrian
通常,您必須知道學科中的alpha。是的,通常是0.05 = 5%。要決定測試,您必須將p值與您的alpha值進行比較。那麼,爲什麼你想看到這麼小的p值呢? https://en.wikipedia.org/wiki/P-value – jogo