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How to conduct a repeated measures ANOVA (in R, using rstatix and tidyr and car)

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Task

In a repeated measures test, the same subject receives multiple treatments. When you have a dataset that includes the responses of a repeated measures test where the measurements are dependent (within subjects design), you may wish to check if there is a difference in the treatment effects. How would you conduct a repeated measures ANOVA to answer that question?

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Solution

We create a hypothetical repeated measures dataset where the 5 subjects undergo all 4 skin treatments and their rating of the treatment is measured.

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subject <- as.factor(c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5))
skin.treatment <- c('W','X','Y','Z','W','X','Y','Z','W','X',
                    'Y','Z','W','X','Y','Z','W','X','Y','Z')
rating <- c(7,5,8,4,8,10,7,5,7,6,5,4,7,7,4,5,8,8,6,6)
df <- data.frame(subject,skin.treatment,rating)
head(df)

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  subject skin.treatment rating
1 1       W               7    
2 1       X               5    
3 1       Y               8    
4 1       Z               4    
5 2       W               8    
6 2       X              10

Before we conduct a repeated measures ANOVA, we need to decide which approach to use - Univariate or Multivariate. We decide this using Mauchly’s test of sphericity. If we fail to reject the null hypothesis then we use the univariate approach.

  • $H_0 =$ the sphericity assumption holds
  • $H_A =$ the sphericity assumption is violated

We use the rstatix package to conduct the test.

  • The dependent variable is rating.
  • The within-group factor is skin.treatment.
  • The Error() term is critical in differentiating between a between subjects and within subjects model. It tells R that there is one observation per subject for each level of skin.treatment.
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# install.packages("rstatix") # If you have not already installed it
library(rstatix)
anova_test(rating ~ skin.treatment + Error(subject/skin.treatment), data=df)

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Attaching package: ‘rstatix’


The following object is masked from ‘package:stats’:

    filter





ANOVA Table (type III tests)

$ANOVA
          Effect DFn DFd     F     p p<.05  ges
1 skin.treatment   3  12 5.118 0.017     * 0.43

$`Mauchly's Test for Sphericity`
          Effect     W     p p<.05
1 skin.treatment 0.062 0.207      

$`Sphericity Corrections`
          Effect   GGe     DF[GG] p[GG] p[GG]<.05   HFe     DF[HF] p[HF]
1 skin.treatment 0.541 1.62, 6.49 0.051           0.858 2.57, 10.3 0.023
  p[HF]<.05
1         *

The $p$-value we care about in the output is under “Macuhly’s test for sphericity,” for the variable skin.treatment. Because the $p$-value is 0.207, we fail to reject the sphericity assumption at a 5% significance level and use the univariate approach. to conduct the repeated measures ANOVA.

Repeated measures ANOVA - univariate 

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aov1 <- aov(rating ~ skin.treatment + Error(subject/skin.treatment), data=df)
summary(aov1)

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Error: subject
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals  4   11.8    2.95               

Error: subject:skin.treatment
               Df Sum Sq Mean Sq F value Pr(>F)  
skin.treatment  3  21.75   7.250   5.118 0.0165 *
Residuals      12  17.00   1.417                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

You can find the $p$-value at the end of the row of output marked for skin.treatment; it is 0.0165. This is less than 0.05, so we conclude that there is significant evidence of a treatment effect.

Repeated measures ANOVA - multivariate

If instead the first test had rejected the sphericity assumption, we would have used a multivariate approach for the repeated measures ANOVA. We show here how to do such a test, even though it does not apply to this situation. We must first reorganize the data into a matrix where each row represents a single subject, and columns represent levels of the treatment factor. This is possible using the tidyr package.

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# install.packages("tidyr") # If you have not already installed it
library(tidyr)
multi.data <- spread(df, skin.treatment, rating)
multi.data <- as.matrix(multi.data[,-c(1)])
multi.data

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     W X  Y Z
[1,] 7  5 8 4
[2,] 8 10 7 5
[3,] 7  6 5 4
[4,] 7  7 4 5
[5,] 8  8 6 6

We then create a multivariate model and also set up a variable that defines the design of the study.

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# In this model there are no between-subjects factors, so we write ~ 1:
multi.ml <- lm(multi.data ~ 1)
# The design of the study is a single factor with four levels:
rfactor <- factor(c("f1", "f2", "f3", "f4"))

Conduct the repeated measures ANOVA using a multivariate approach. This requires creating a new model using the Anova() function that calculates ANOVA tables. The car package provides the Anova() function. The parameters have the following meanings.

  • idata includes information about the number of levels, in this case four.
  • idesign states that rfactor describes a repeated-measures variable.
  • type tells Anova() to calculate the “Type-III” sums of squares when forming the ANOVA table.
  • multivariate suppresses output about multivariate statistical tests, which are relevant only when the experimental design includes multiple dependent variables.
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# install.packages("car") # If you have not already installed it
library(car)
multi.ml <- Anova(multi.ml, idata=data.frame(rfactor), idesign = ~rfactor, type="III")
summary(multi.ml, multivariate=FALSE)

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Loading required package: carData





Univariate Type III Repeated-Measures ANOVA Assuming Sphericity

            Sum Sq num Df Error SS den Df  F value    Pr(>F)    
(Intercept) 806.45      1     11.8      4 273.3729 7.837e-05 ***
rfactor      21.75      3     17.0     12   5.1176    0.0165 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Mauchly Tests for Sphericity

        Test statistic p-value
rfactor       0.062101 0.20708


Greenhouse-Geisser and Huynh-Feldt Corrections
 for Departure from Sphericity

        GG eps Pr(>F[GG])  
rfactor 0.5412    0.05068 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

          HF eps Pr(>F[HF])
rfactor 0.858156 0.02319302

Although this test was run just as an example, and does not actually apply in this dataset, the output shows a $p$-value of 0.0165, at the end of the first rfactor row. That $p$-value could be compared to a chosen $\alpha$.

(We also see that Mauchly’s test was performed, which is not significant, and is the reason this data actually demands a univariate approach.)

Content last modified on 24 July 2023.

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Contributed by Krtin Juneja (KJUNEJA@falcon.bentley.edu)