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,
...
)
```

x

Either a two-way table or a two-way matrix. Can also be a vector of observations.

g

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.

group

If `x`

is a table or matrix, `group`

indicates whether
the `"row"`

or the `"column"`

variable is
the nominal, grouping variable.

coin

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.

ci

If `TRUE`

, returns confidence intervals by bootstrap.
May be slow.

conf

The level for the confidence interval.

type

The type of confidence interval to use.
Can be any of "`norm`

", "`basic`

",
"`perc`

", or "`bca`

".
Passed to `boot.ci`

.

R

The number of replications to use for bootstrap.

histogram

If `TRUE`

, produces a histogram of bootstrapped values.

digits

The number of significant digits in the output.

reportIncomplete

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.

A single statistic, r. Or a small data frame consisting of r, and the lower and upper confidence limits.

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.

```
# NOT RUN {
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 ~ Sex, data = Catbus)
wilcoxonR(x = Catbus$Steps, g = Catbus$Sex)
# }
```

Run the code above in your browser using DataLab