Rank biserial correlation coefficient for one-sample Wilcoxon test

Calculates rank biserial correlation coefficient effect size for one-sample Wilcoxon signed-rank test; confidence intervals by bootstrap.

Usage

wilcoxonOneSampleRC(
  x,
  mu = NULL,
  zero.method = "Wilcoxon",
  ci = FALSE,
  conf = 0.95,
  type = "perc",
  R = 1000,
  histogram = FALSE,
  digits = 3,
  verbose = FALSE,
  ...
)

Arguments

x: A vector of observations.
mu: The value to compare x to, as in wilcox.test
zero.method: If "Wilcoxon", differences of zero are discarded and then ranks are determined. If "Pratt", ranks are determined, and then differences of zero are discarded. If "none", differences of zero are not discarded.
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.
verbose: If TRUE, prints information on sample size and ranks.
...: Additional arguments passed to the wilcoxsign_test function.

Value

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

Details

It is recommended that NAs be removed beforehand.

When rc 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.

References

https://rcompanion.org/handbook/F_02.html

Author

Salvatore Mangiafico, mangiafico@njaes.rutgers.edu

Examples

### Example with one zero difference
A = c(11,12,13,14,15,16,17,18,19,20)
#' wilcoxonOneSampleRC(x = A, mu=15)
wilcoxonOneSampleRC(x = A, mu=15, verbose=TRUE, zero.method="Wilcoxon")
#> 
#> zero.method: Wilcoxon
#> n kept = 9
#> Ranks plus = 18
#> Ranks minus = 27
#> T value = 18
#> 
#>  rc 
#> 0.2 
wilcoxonOneSampleRC(x = A, mu=15, verbose=TRUE, zero.method="Pratt")
#> 
#> zero.method: Pratt
#> n kept = 9
#> Ranks plus = 22
#> Ranks minus = 32
#> T value = 22
#> 
#>    rc 
#> 0.222 
wilcoxonOneSampleRC(x = A, mu=15, verbose=TRUE, zero.method="none")
#> 
#> zero.method: none
#> n kept = 10
#> Ranks plus = 22
#> Ranks minus = 32
#> T value = 22
#> 
#>    rc 
#> 0.182