Sono venuto dal post " Confronti post-hoc a coppie di ANOVA a due vie " (rispondendo a questo post ), che mostra quanto segue:
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.(L'uscita è allegata sotto)
Qualcuno potrebbe spiegare perché Tukey HSD è in grado di trovare un accoppiamento significativo mentre il test t accoppiato (valore non aggiustato) non riesce a farlo?
Grazie.
Ecco l'output del codice
> 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.