pairedSampleDominance {rcompanion}

Dominance statistic for two-sample paired data

Description

Calculates a dominance effect size statistic for two-sample paired data with confidence intervals by bootstrap

Usage

pairedSampleDominance(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  ci = FALSE,
  conf = 0.95,
  type = "perc",
  R = 1000,
  histogram = FALSE,
  digits = 3,
  na.rm = TRUE,
  ...
)

Arguments

formula

A formula indicating the response variable and the independent variable. e.g. y ~ group.

data

The data frame to use.

x

If no formula is given, the response variable for one group.

y

The response variable for the other group.

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.

na.rm

If TRUE, removes NA values from the input vectors or data frame.

...

Additional arguments.

Details

The calculated Dominance statistic is simply the proportion of observations in x greater the paired observations in y, minus the proportion of observations in x less than the paired observations in y

It will range from -1 to 1, with and 1 indicating that the all the observations in x are greater than the paired observations in y, and -1 indicating that the all the observations in y are greater than the paired observations in x.

The input should include either formula and data; or x, and y. If there are more than two groups, only the first two groups are used.

This statistic is appropriate for truly ordinal data, and could be considered an effect size statistic for a two-sample paired 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

Value

A small data frame consisting of descriptive statistics, the dominance statistic, and potentially the lower and upper confidence limits.

Author(s)

Salvatore Mangiafico, [e-mail]

References

https://rcompanion.org/handbook/F_07.html

See Also

oneSampleDominance, vda, cliffDelta

Examples

data(Pooh)
Time.1 = Pooh$Likert[Pooh$Time == 1]
Time.2 = Pooh$Likert[Pooh$Time == 2]
library(DescTools)
SignTest(x = Time.1, y = Time.2)
pairedSampleDominance(x = Time.1, y = Time.2)
pairedSampleDominance(Likert ~ Time, data=Pooh)