library(readxl)
pa <- read_xlsx("D:/2.RESEARCH/0. Projects/MP38_Kha/PA_for_R.xlsx")#Anova
anova.pa = aov(mfi ~ group, data = pa)
summary(anova.pa)## Df Sum Sq Mean Sq F value Pr(>F)
## group 5 107.7 21.542 30.94 <2e-16 ***
## Residuals 378 263.2 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis
tukey.anova.pa= TukeyHSD(anova.pa)
options(digits= 4)
tukey.anova.pa## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = mfi ~ group, data = pa)
##
## $group
## diff lwr upr p adj
## h_native-h_heat 0.16839 -0.25413 0.5909 0.8637
## k_heat-h_heat 0.61735 0.18000 1.0547 0.0009
## k_native-h_heat 0.53894 0.10159 0.9763 0.0062
## v_heat-h_heat 1.60219 1.19158 2.0128 0.0000
## v_native-h_heat 0.67708 0.26646 1.0877 0.0000
## k_heat-h_native 0.44897 0.01161 0.8863 0.0404
## k_native-h_native 0.37055 -0.06680 0.8079 0.1496
## v_heat-h_native 1.43381 1.02319 1.8444 0.0000
## v_native-h_native 0.50869 0.09807 0.9193 0.0058
## k_native-k_heat -0.07841 -0.53011 0.3733 0.9963
## v_heat-k_heat 0.98484 0.55898 1.4107 0.0000
## v_native-k_heat 0.05972 -0.36614 0.4856 0.9986
## v_heat-k_native 1.06325 0.63739 1.4891 0.0000
## v_native-k_native 0.13814 -0.28772 0.5640 0.9388
## v_native-v_heat -0.92512 -1.32347 -0.5268 0.0000