wilcox_TOST.Rd
A function for TOST using the non-parametric methods of the Wilcoxon-Mann-Whitney family of tests. This function uses the normal approximation and applies a continuity correction automatically.
wilcox_TOST(
x,
...,
hypothesis = "EQU",
paired = FALSE,
eqb,
low_eqbound,
high_eqbound,
ses = "rb",
alpha = 0.05
)
# S3 method for default
wilcox_TOST(
x,
y = NULL,
hypothesis = "EQU",
paired = FALSE,
eqb,
low_eqbound,
high_eqbound,
ses = c("rb", "odds", "cstat"),
alpha = 0.05,
mu = 0,
...
)
# S3 method for formula
wilcox_TOST(formula, data, subset, na.action, ...)
a (non-empty) numeric vector of data values.
further arguments to be passed to or from methods.
'EQU' for equivalence (default), or 'MET' for minimal effects test, the alternative hypothesis.
a logical indicating whether you want a paired t-test.
Equivalence bound. Can provide 1 value (negative value is taken as the lower bound) or 2 specific values that represent the upper and lower equivalence bounds.
lower equivalence bounds (deprecated).
upper equivalence bounds (deprecated).
Standardized effect size. Default is "rb" for rank-biserial correlation. Options also include "cstat" for concordance probability, or "odds" for Wilcoxon-Mann-Whitney odds (otherwise known as Agresti's generalized odds ratio).
alpha level (default = 0.05)
an optional (non-empty) numeric vector of data values.
number indicating the value around which (a-)symmetry (for one-sample or paired samples) or shift (for independent samples) is to be estimated. See stats::wilcox.test.
a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs either 1 for a one-sample or paired test or a factor with two levels giving the corresponding groups. If lhs is of class "Pair" and rhs is 1, a paired test is done.
an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").
An S3 object of class
"TOSTnp"
is returned containing the following slots:
"TOST": A table of class "data.frame"
containing two-tailed wilcoxon signed rank test and both one-tailed results.
"eqb": A table of class "data.frame"
containing equivalence bound settings.
"effsize": table of class "data.frame"
containing effect size estimates.
"hypothesis": String stating the hypothesis being tested.
"smd": List containing information on standardized effect size.
"alpha": Alpha level set for the analysis.
"method": Type of non-parametric test.
"decision": List included text regarding the decisions for statistical inference.
For details on the calculations in this function see vignette("robustTOST")
.
If only x is given, or if both x and y are given and paired is TRUE, a Wilcoxon signed rank test of the null that the distribution of x (in the one sample case) or of x - y (in the paired two sample case) is symmetric about mu is performed.
Otherwise, if both x and y are given and paired is FALSE, a Wilcoxon rank sum test (equivalent to the Mann-Whitney test: see the Note) is carried out. In this case, the null hypothesis is that the distributions of x and y differ by a location shift.
David F. Bauer (1972). Constructing confidence sets using rank statistics. Journal of the American Statistical Association 67, 687–690. doi: 10.1080/01621459.1972.10481279.
Myles Hollander and Douglas A. Wolfe (1973). Nonparametric Statistical Methods. New York: John Wiley & Sons. Pages 27–33 (one-sample), 68–75 (two-sample). Or second edition (1999).
Other Robust tests:
boot_log_TOST()
,
boot_t_TOST()
,
boot_t_test()
,
brunner_munzel()
,
log_TOST()
Other TOST:
boot_log_TOST()
,
boot_t_TOST()
,
simple_htest()
,
t_TOST()
,
tsum_TOST()
data(mtcars)
wilcox_TOST(mpg ~ am,
data = mtcars,
eqb = 3)
#>
#> Wilcoxon rank sum test with continuity correction
#>
#> The equivalence test was non-significant W = 18.500, p = 9.75e-01
#> The null hypothesis test was significant W = 42.000, p = 1.87e-03
#> NHST: reject null significance hypothesis that the effect is equal to zero
#> TOST: don't reject null equivalence hypothesis
#>
#> TOST Results
#> Test Statistic p.value
#> NHST 42.0 0.002
#> TOST Lower 73.0 0.975
#> TOST Upper 18.5 < 0.001
#>
#> Effect Sizes
#> Estimate C.I. Conf. Level
#> Median of Differences -6.8000 [-10.9999, -3.6001] 0.9
#> Rank-Biserial Correlation -0.6599 [-0.8143, -0.4182] 0.9
#>
#>