wilcoxonPairedRC {rcompanion} | R Documentation |
Matched-pairs rank biserial correlation coefficient
Description
Calculates matched-pairs rank biserial correlation coefficient effect size for paired Wilcoxon signed-rank test; confidence intervals by bootstrap.
Usage
wilcoxonPairedRC(
x,
g = 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. |
g |
The vector of observations for the grouping, nominal variable. Only the first two levels of the nominal variable are used. |
zero.method |
If |
ci |
If |
conf |
The level for the confidence interval. |
type |
The type of confidence interval to use.
Can be any of " |
R |
The number of replications to use for bootstrap. |
histogram |
If |
digits |
The number of significant digits in the output. |
verbose |
If |
... |
Additional arguments passed to |
Details
It is recommended that NA
s be removed
beforehand.
When the data in the first group are greater than in the second group, rc is positive. When the data in the second group are greater than in the first group, rc is negative.
Be cautious with this interpretation, as R will alphabetize
groups if g
is not already a factor.
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.
Value
A single statistic, rc. Or a small data frame consisting of rc, and the lower and upper confidence limits.
Author(s)
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
References
King, B.M., P.J. Rosopa, and E.W. Minium. 2011. Statistical Reasoning in the Behavioral Sciences, 6th ed.
https://rcompanion.org/handbook/F_06.html
See Also
Examples
data(Pooh)
Time1 = Pooh$Likert[Pooh$Time==1]
Time2 = Pooh$Likert[Pooh$Time==2]
wilcox.test(x = Time1, y = Time2, paired=TRUE, exact=FALSE)
wilcoxonPairedRC(x = Pooh$Likert, g = Pooh$Time)
### Example from King, Rosopa, and Minium
Placebo = c(24,39,29,28,25,32,31,33,31,22)
Drug = c(28,29,34,21,28,15,17,28,16,12)
Y = c(Placebo, Drug)
Group = factor(c(rep("Placebo", length(Placebo)),
rep("Drug", length(Drug))),
levels=c("Placebo", "Drug"))
wilcoxonPairedRC(x = Y, g = Group)
### Example with some zero differences
A = c(11,12,13,14,15,16,17,18,19,20)
B = c(12,14,16,18,20,22,12,10,19,20)
Y = c(A, B)
Group = factor(c(rep("A", length(A)),
rep("B", length(B))))
wilcoxonPairedRC(x = Y, g = Group, verbose=TRUE, zero.method="Wilcoxon")
wilcoxonPairedRC(x = Y, g = Group, verbose=TRUE, zero.method="Pratt")
wilcoxonPairedRC(x = Y, g = Group, verbose=TRUE, zero.method="none")