Making text subtitle for the Mann-Whitney U-test (between-subjects designs).
subtitle_mann_nonparametric(data, x, y, paired = FALSE, k = 2,
conf.level = 0.95, messages = TRUE, ...)subtitle_t_nonparametric(data, x, y, paired = FALSE, k = 2,
conf.level = 0.95, messages = TRUE, ...)
A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.
The grouping variable from the dataframe data.
The response (a.k.a. outcome or dependent) variable from the
dataframe data.
a logical indicating whether you want a paired t-test.
Number of digits after decimal point (should be an integer)
(Default: k = 2).
Scalar between 0 and 1. If unspecified, the defaults return
95% lower and upper confidence intervals (0.95).
Decides whether messages references, notes, and warnings are
to be displayed (Default: TRUE).
Additional arguments.
Two-sample Wilcoxon test, also known as Mann-Whitney test, is
carried out. The effect size estimate for this test is Spearman's rho
as the ranks of the y variable related to the factor x.
For the two independent samples case, the Mann Whitney U-test is calculated and W is reported from stats::wilcox.test. For the paired samples case the Wilcoxon signed rank test is run and V is reported.
Since there is no single commonly accepted method for reporting effect size for these tests we are computing and reporting Spearman's rho a.k.a. r along with the confidence intervals associated with the estimate.
We have selected Spearman's rho which should be nearly identical to rank bi-serial and Somer's d for the case of x as two factors (including) as a pre/post measure and with y treated as ranks rather than raw scores.
# NOT RUN {
subtitle_mann_nonparametric(
data = sleep,
x = group,
y = extra
)
# }
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