Provides a pipe-friendly framework to perform a Friedman rank sum test, which is the non-parametric alternative to the one-way repeated measures ANOVA test. Wrapper around the function friedman.test().

friedman_test(data, formula, ...)

Arguments

data

a data.frame containing the variables in the formula.

formula

a formula of the form a ~ b | c, where a (numeric) is the dependent variable name; b is the within-subjects factor variables; and c (factor) is the column name containing individuals/subjects identifier. Should be unique per individual.

...

other arguments to be passed to the function friedman.test.

Value

return a data frame with the following columns:

  • .y.: the y (dependent) variable used in the test.

  • n: sample count.

  • statistic: the value of Friedman's chi-squared statistic, used to compute the p-value.

  • p: p-value.

  • method: the statistical test used to compare groups.

Examples

# Load data #::::::::::::::::::::::::::::::::::::::: data("ToothGrowth") df <- ToothGrowth %>% filter(supp == "VC") %>% mutate(id = rep(1:10, 3)) head(df)
#> len supp dose id #> 1 4.2 VC 0.5 1 #> 2 11.5 VC 0.5 2 #> 3 7.3 VC 0.5 3 #> 4 5.8 VC 0.5 4 #> 5 6.4 VC 0.5 5 #> 6 10.0 VC 0.5 6
# Friedman rank sum test #::::::::::::::::::::::::::::::::::::::::: df %>% friedman_test(len ~ dose | id)
#> # A tibble: 1 x 6 #> .y. n statistic df p method #> * <chr> <int> <dbl> <dbl> <dbl> <chr> #> 1 len 10 20 2 0.0000454 Friedman test