Compute the effect size estimate (referred to as `w`

) for
Friedman test: `W = X2/N(K-1)`

; where `W`

is the Kendall's W
value; `X2`

is the Friedman test statistic value; `N`

is the sample
size. `k`

is the number of measurements per subject.

The Kendall’s W coefficient assumes the value from 0 (indicating no
relationship) to 1 (indicating a perfect relationship).

Kendalls uses the Cohen’s interpretation guidelines of `0.1 - < 0.3`

(small
effect), `0.3 - < 0.5`

(moderate effect) and `>= 0.5`

(large
effect)

Confidence intervals are calculated by bootstap.

friedman_effsize(data, formula, ci = FALSE, conf.level = 0.95,
ci.type = "perc", nboot = 1000, ...)

## 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. |

ci |
If TRUE, returns confidence intervals by bootstrap. May be slow. |

conf.level |
The level for the confidence interval. |

ci.type |
The type of confidence interval to use. Can be any of "norm",
"basic", "perc", or "bca". Passed to `boot::boot.ci` . |

nboot |
The number of replications to use for bootstrap. |

... |
other arguments passed to the function `friedman.test()` |

## Value

return a data frame with some of the following columns:

`.y.`

: the y variable used in the test.

`n`

: Sample
counts.

`effsize`

: estimate of the effect size.

`magnitude`

: magnitude of effect size.

`conf.low,conf.high`

:
lower and upper bound of the effect size confidence interval.

## References

Maciej Tomczak and Ewa Tomczak. The need to report effect size
estimates revisited. An overview of some recommended measures of effect
size. Trends in Sport Sciences. 2014; 1(21):19-25.

## Examples

#> 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 test effect size
#:::::::::::::::::::::::::::::::::::::::::
df %>% friedman_effsize(len ~ dose | id)

#> # A tibble: 1 x 5
#> .y. n effsize method magnitude
#> * <chr> <int> <dbl> <chr> <ord>
#> 1 len 10 1 Kendall W large