Calculates r effect size for Mann-Whitney two-sample rank-sum test, or a table with an ordinal variable and a nominal variable with two levels; confidence intervals by bootstrap.
wilcoxonR(
x,
g = NULL,
group = "row",
coin = FALSE,
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
digits = 3,
reportIncomplete = FALSE,
...
)
A single statistic, r. Or a small data frame consisting of r, and the lower and upper confidence limits.
Either a two-way table or a two-way matrix. Can also be a vector of observations.
If x
is a vector, g
is the vector of observations for
the grouping, nominal variable.
Only the first two levels of the nominal variable are used.
If x
is a table or matrix, group
indicates whether
the "row"
or the "column"
variable is
the nominal, grouping variable.
If FALSE
, the default, the Z value
is extracted from a function similar to the
wilcox.test
function in the stats package.
If TRUE
, the Z value
is extracted from the wilcox_test
function in the
coin package. This method may be much slower, especially
if a confidence interval is produced.
If TRUE
, returns confidence intervals by bootstrap.
May be slow.
The level for the confidence interval.
The type of confidence interval to use.
Can be any of "norm
", "basic
",
"perc
", or "bca
".
Passed to boot.ci
.
The number of replications to use for bootstrap.
If TRUE
, produces a histogram of bootstrapped values.
The number of significant digits in the output.
If FALSE
(the default),
NA
will be reported in cases where there
are instances of the calculation of the statistic
failing during the bootstrap procedure.
Additional arguments passed to the wilcox_test
function.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
r is calculated as Z divided by square root of the total observations.
This statistic reports a smaller effect size than does
Glass rank biserial correlation coefficient
(wilcoxonRG
), and cannot reach
-1 or 1. This effect is exaserbated when sample sizes
are not equal.
Currently, the function makes no provisions for NA
values in the data. It is recommended that NA
s be removed
beforehand.
When the data in the first group are greater than
in the second group, r is positive.
When the data in the second group are greater than
in the first group, r is negative.
Be cautious with this interpretation, as R will alphabetize
groups if g
is not already a factor.
When r is close to extremes, or with small counts in some cells, the confidence intervals determined by this method may not be reliable, or the procedure may fail.
freemanTheta
,
wilcoxonRG
data(Breakfast)
Table = Breakfast[1:2,]
library(coin)
chisq_test(Table, scores = list("Breakfast" = c(-2, -1, 0, 1, 2)))
wilcoxonR(Table)
data(Catbus)
wilcox.test(Steps ~ Gender, data = Catbus)
wilcoxonR(x = Catbus$Steps, g = Catbus$Gender)
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