rank_epsilon_squared  R Documentation 
Compute rank epsilon squared (E^2_R) or rank eta squared
(η^2_H) (to accompany stats::kruskal.test()
), and Kendall's W
(to accompany stats::friedman.test()
) effect sizes for nonparametric (rank
sum) oneway ANOVAs.
rank_epsilon_squared( x, groups, data = NULL, ci = 0.95, alternative = "greater", iterations = 200, verbose = TRUE, ... ) rank_eta_squared( x, groups, data = NULL, ci = 0.95, alternative = "greater", iterations = 200, verbose = TRUE, ... ) kendalls_w( x, groups, blocks, data = NULL, blocks_on_rows = TRUE, ci = 0.95, alternative = "greater", iterations = 200, verbose = TRUE, ... )
x 
Can be one of:

groups, blocks 
A factor vector giving the group / block for the
corresponding elements of 
data 
An optional data frame containing the variables. 
ci 
Confidence Interval (CI) level 
alternative 
a character string specifying the alternative hypothesis;
Controls the type of CI returned: 
iterations 
The number of bootstrap replicates for computing confidence
intervals. Only applies when 
verbose 
Toggle warnings and messages on or off. 
... 
Arguments passed to or from other methods. When 
blocks_on_rows 
Are blocks on rows ( 
The rank epsilon squared and rank eta squared are appropriate for
nonparametric tests of differences between 2 or more samples (a rank based
ANOVA). See stats::kruskal.test. Values range from 0 to 1, with larger
values indicating larger differences between groups.
Kendall's W is appropriate for nonparametric tests of differences between
2 or more dependent samples (a rank based rmANOVA), where each group
(e.g.,
experimental condition) was measured for each block
(e.g., subject). This
measure is also common as a measure of reliability of the rankings of the
groups
between raters (blocks
). See stats::friedman.test. Values range
from 0 to 1, with larger values indicating larger differences between groups
/ higher agreement between raters.
A data frame with the effect size and its CI.
Confidence intervals for E^2_R, η^2_H, and Kendall's W are
estimated using the bootstrap method (using the {boot}
package).
"Confidence intervals on measures of effect size convey all the information
in a hypothesis test, and more." (Steiger, 2004). Confidence (compatibility)
intervals and p values are complementary summaries of parameter uncertainty
given the observed data. A dichotomous hypothesis test could be performed
with either a CI or a p value. The 100 (1  α)% confidence
interval contains all of the parameter values for which p > α
for the current data and model. For example, a 95% confidence interval
contains all of the values for which p > .05.
Note that a confidence interval including 0 does not indicate that the null
(no effect) is true. Rather, it suggests that the observed data together with
the model and its assumptions combined do not provided clear evidence against
a parameter value of 0 (same as with any other value in the interval), with
the level of this evidence defined by the chosen α level (Rafi &
Greenland, 2020; Schweder & Hjort, 2016; Xie & Singh, 2013). To infer no
effect, additional judgments about what parameter values are "close enough"
to 0 to be negligible are needed ("equivalence testing"; Bauer & Kiesser,
1996).
Some effect sizes are directionless–they do have a minimum value that would
be interpreted as "no effect", but they cannot cross it. For example, a null
value of Kendall's W is 0, indicating no difference between
groups, but it can never have a negative value. Same goes for
U2 and Overlap: the null value of U_2 is
0.5, but it can never be smaller than 0.5; am Overlap of 1 means "full
overlap" (no difference), but it cannot be larger than 1.
When bootstrapping CIs for such effect sizes, the bounds of the CIs will
never cross (and often will never cover) the null. Therefore, these CIs
should not be used for statistical inference.
When tied values occur, they are each given the average of the ranks that would have been given had no ties occurred. This results in an effect size of reduced magnitude. A correction has been applied for Kendall's W.
Kendall, M.G. (1948) Rank correlation methods. London: Griffin.
Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends in sport sciences, 1(21), 1925.
Other rankbased effect sizes:
p_superiority()
,
rank_biserial()
Other effect sizes for ANOVAs:
eta_squared()
# Rank Eta/Epsilon Squared # ======================== rank_eta_squared(mpg ~ cyl, data = mtcars) rank_epsilon_squared(mpg ~ cyl, data = mtcars) # Kendall's W # =========== dat < data.frame( cond = c("A", "B", "A", "B", "A", "B"), ID = c("L", "L", "M", "M", "H", "H"), y = c(44.56, 28.22, 24, 28.78, 24.56, 18.78) ) (W < kendalls_w(y ~ cond  ID, data = dat, verbose = FALSE)) interpret_kendalls_w(0.11) interpret(W, rules = "landis1977")
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