나는 " 양방향 ANOVA의 사후 쌍별 비교 "( 이 게시물에 해당 ) 게시물 을 보았습니다 .
dataTwoWayComparisons <- read.csv("http://www.dailyi.org/blogFiles/RTutorialSeries/dataset_ANOVA_TwoWayComparisons.csv")
model1 <- aov(StressReduction~Treatment+Age, data =dataTwoWayComparisons)
summary(model1) # Treatment is signif
pairwise.t.test(dataTwoWayComparisons$StressReduction, dataTwoWayComparisons$Treatment, p.adj = "none")
# no signif pair
TukeyHSD(model1, "Treatment")
# mental-medical is the signif pair.(출력은 아래에 첨부)
Tukey HSD가 쌍을 이루는 (값이 조정되지 않은 pvalue) t- 검정이 실패하는 동안 왜 중요한 쌍을 찾을 수 있는지 설명해 주시겠습니까?
감사.
코드 출력은 다음과 같습니다
> model1 <- aov(StressReduction~Treatment+Age, data =dataTwoWayComparisons)
> summary(model1) # Treatment is signif
Df Sum Sq Mean Sq F value Pr(>F)
Treatment 2 18 9.000 11 0.0004883 ***
Age 2 162 81.000 99 1e-11 ***
Residuals 22 18 0.818
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> pairwise.t.test(dataTwoWayComparisons$StressReduction, dataTwoWayComparisons$Treatment, p.adj = "none")
Pairwise comparisons using t tests with pooled SD
data: dataTwoWayComparisons$StressReduction and dataTwoWayComparisons$Treatment
medical mental
mental 0.13 -
physical 0.45 0.45
P value adjustment method: none
> # no signif pair
>
> TukeyHSD(model1, "Treatment")
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = StressReduction ~ Treatment + Age, data = dataTwoWayComparisons)
$Treatment
diff lwr upr p adj
mental-medical 2 0.92885267 3.07114733 0.0003172
physical-medical 1 -0.07114733 2.07114733 0.0702309
physical-mental -1 -2.07114733 0.07114733 0.0702309
> # mental-medical is the signif pair.