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How to perform tukey HSD in base R?
First thing you must remember while moving on to post hoc analysis is the null hypothesis of the analysis of variance must be rejected, so that we can claim there exists a difference in the group means. Now, once we achieve that the tukey HSD can be performed simply by using TukeyHSD function in base R.
Example
Consider the below data frame −
x1<-rep(LETTERS[1:4],5) y1<-rep(c(5,2000,30,99),5) df1<-data.frame(x1,y1) df1
Output
x1 y1 1 A 5 2 B 2000 3 C 30 4 D 99 5 A 5 6 B 2000 7 C 30 8 D 99 9 A 5 10 B 2000 11 C 30 12 D 99 13 A 5 14 B 2000 15 C 30 16 D 99 17 A 5 18 B 2000 19 C 30 20 D 99
Example
Performing analysis of variance −
ANOVA<-aov(y1~x1,data=df1) summary(ANOVA)
Output
Df Sum Sq Mean Sq F value Pr(>F) x1 3 14361185 4787062 1.07e+32 <2e-16 *** Residuals 16 0 0 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Example
Performing tukey HSD −
TukeyHSD(ANOVA)
Output
Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = y1 ~ x1, data = df1) $x1 diff lwr upr p adj B-A 1995 1995 1995 0 C-A 25 25 25 0 D-A 94 94 94 0 C-B -1970 -1970 -1970 0 D-B -1901 -1901 -1901 0 D-C 69 69 69 0
Example
Consider the PlantGrowth data in base R −
str(PlantGrowth)
Output
'data.frame': 30 obs. of 2 variables: $ weight: num 4.17 5.58 5.18 6.11 4.5 4.61 5.17 4.53 5.33 5.14 ... $ group : Factor w/ 3 levels "ctrl","trt1",..: 1 1 1 1 1 1 1 1 1 1 ...
Example
head(PlantGrowth,20)
Output
weight group 1 4.17 ctrl 2 5.58 ctrl 3 5.18 ctrl 4 6.11 ctrl 5 4.50 ctrl 6 4.61 ctrl 7 5.17 ctrl 8 4.53 ctrl 9 5.33 ctrl 10 5.14 ctrl 11 4.81 trt1 12 4.17 trt1 13 4.41 trt1 14 3.59 trt1 15 5.87 trt1 16 3.83 trt1 17 6.03 trt1 18 4.89 trt1 19 4.32 trt1 20 4.69 trt1
Performing analysis of variance −
Example
ANOVA<-aov(weight~group,data=PlantGrowth) summary(ANOVA)
Output
Df Sum Sq Mean Sq F value Pr(>F) group 2 3.766 1.8832 4.846 0.0159 * Residuals 27 10.492 0.3886 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Example
Performing tukey HSD −
TukeyHSD(ANOVA)
Output
Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = weight ~ group, data = PlantGrowth) $group diff lwr upr p adj trt1-ctrl -0.371 -1.0622161 0.3202161 0.3908711 trt2-ctrl 0.494 -0.1972161 1.1852161 0.1979960 trt2-trt1 0.865 0.1737839 1.5562161 0.0120064
Updated on: 08-Dec-2020
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