ANOVA in R

用R进行方差分析

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1 Setup

# Load the necessary packages

library(tidyverse)
View(chickwts)

2 Visualizing the data

chickwts |> 
  ggplot(aes(x = feed, y = weight)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter() +
  theme_minimal()

3 ANOVA

model <- aov(weight ~ feed, data = chickwts)

summary(model)

            Df Sum Sq Mean Sq F value   Pr(>F)    
feed         5 231129   46226   15.37 5.94e-10 ***
Residuals   65 195556    3009                     
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Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

4 Post-hoc tests

TukeyHSD(model)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = weight ~ feed, data = chickwts)

$feed
                           diff         lwr       upr     p adj
horsebean-casein    -163.383333 -232.346876 -94.41979 0.0000000
linseed-casein      -104.833333 -170.587491 -39.07918 0.0002100
meatmeal-casein      -46.674242 -113.906207  20.55772 0.3324584
soybean-casein       -77.154762 -140.517054 -13.79247 0.0083653
sunflower-casein       5.333333  -60.420825  71.08749 0.9998902
linseed-horsebean     58.550000  -10.413543 127.51354 0.1413329
meatmeal-horsebean   116.709091   46.335105 187.08308 0.0001062
soybean-horsebean     86.228571   19.541684 152.91546 0.0042167
sunflower-horsebean  168.716667   99.753124 237.68021 0.0000000
meatmeal-linseed      58.159091   -9.072873 125.39106 0.1276965
soybean-linseed       27.678571  -35.683721  91.04086 0.7932853
sunflower-linseed    110.166667   44.412509 175.92082 0.0000884
soybean-meatmeal     -30.480519  -95.375109  34.41407 0.7391356
sunflower-meatmeal    52.007576  -15.224388 119.23954 0.2206962
sunflower-soybean     82.488095   19.125803 145.85039 0.0038845