wilcoxonPairedR | R Documentation |
Calculates r effect size for a Wilcoxon two-sample paired signed-rank test; confidence intervals by bootstrap.
wilcoxonPairedR(
x,
g = NULL,
adjustn = TRUE,
coin = FALSE,
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
cases = TRUE,
digits = 3,
...
)
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. The data must be ordered so that the first observation of the of the first group is paired with the first observation of the second group. |
adjustn |
If |
coin |
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 |
cases |
By default the |
digits |
The number of significant digits in the output. |
... |
Additional arguments passed to the |
r is calculated as Z divided by
square root of the number of observations in one group. This
results in a statistic that ranges from -1 to 1.
This range doesn't hold if cases=FALSE
.
This statistic typically reports a smaller effect size
(in absolute value) than does
the matched-pairs rank biserial correlation coefficient
(wilcoxonPairedRC
), and may not reach a value
of -1 or 1 if there are ties in the paired differences.
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.
A single statistic, r. Or a small data frame consisting of r, and the lower and upper confidence limits.
My thanks to Peter Stikker for the suggestion to adjust the sample size for ties.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
https://rcompanion.org/handbook/F_06.html
wilcoxonPairedRC
data(Pooh)
Time1 = Pooh$Likert[Pooh$Time==1]
Time2 = Pooh$Likert[Pooh$Time==2]
wilcox.test(x = Time1, y = Time2, paired=TRUE, exact=FALSE)
wilcoxonPairedR(x = Pooh$Likert, g = Pooh$Time)
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