Calculates a dominance effect size statistic compared with a theoretical median for one-sample data with confidence intervals by bootstrap
oneSampleDominance(
x,
mu = 0,
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
digits = 3,
na.rm = TRUE,
...
)A small data frame consisting of descriptive statistics, the dominance statistic, and potentially the lower and upper confidence limits.
A vector of numeric values.
The median against which to compare the values.
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.
If TRUE, removes NA values from
the input vector x.
Additional arguments.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
The calculated Dominance statistic is simply
the proportion of observations greater than mu minus the
the proportion of observations less than mu.
It will range from -1 to 1, with 0 indicating that the median is
equal to mu,
and 1 indicating that the observations are all greater in value
than mu,
and -1 indicating that the observations are all less in value
than mu.
This statistic is appropriate for truly ordinal data, and could be considered an effect size statistic for a one-sample sign test.
Ordered category data need to re-coded as
numeric, e.g. as with as.numeric(Ordinal.variable).
When the statistic is close to 1 or close to -1, or with small sample size, the confidence intervals determined by this method may not be reliable, or the procedure may fail.
VDA is the analogous statistic, converted to a probability,
ranging from 0 to 1, specifically,
VDA = Dominance / 2 + 0.5.
pairedSampleDominance,
cliffDelta,
vda
data(Catbus)
library(DescTools)
SignTest(Catbus$Rating, mu=5.5)
oneSampleDominance(Catbus$Rating, mu=5.5)
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