Calculates r effect size for a Wilcoxon one-sample signed-rank test; confidence intervals by bootstrap.
wilcoxonOneSampleR(
x,
mu = NULL,
adjustn = TRUE,
coin = FALSE,
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
digits = 3,
...
)A single statistic, r. Or a small data frame consisting of r, and the lower and upper confidence limits.
A vector of observations.
The value to compare x to, as in wilcox.test
If TRUE, reduces the sample size in the calculation
of r by the number of observations equal to
mu.
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.
Additional arguments passed to the wilcoxsign_test function.
My thanks to
Peter Stikker for the suggestion to adjust the sample size
for ties with mu.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
r is calculated as Z divided by square root of the number of observations.
The calculated statistic is equivalent to the statistic returned
by the wilcoxPairedR function with one group equal
to a vector of mu.
The author knows of no reference for this technique.
This statistic typically reports a smaller effect size
(in absolute value) than does
the matched-pairs rank biserial correlation coefficient
(wilcoxonOneSampleRC), and may not reach a value
of -1 or 1 if there are values tied with mu.
Currently, the function makes no provisions for NA
values in the data. It is recommended that NAs be removed
beforehand.
When the data are greater than mu, r is positive.
When the data are less than mu, r is negative.
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.
X = c(1,2,3,3,3,3,4,4,4,4,4,5,5,5,5,5)
wilcox.test(X, mu=3, exact=FALSE)
wilcoxonOneSampleR(X, mu=3)
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