twocorci.nov: Compare two dependent correlations: non-overlapping case

In GRousselet/bootcorci: Bootstrap correlation confidence intervals

Description

Compute a 1-alpha percentile bootstrap confidence interval for the difference between two correlation coefficients corresponding to two dependent groups, in the non-overlapping case. Compare correlation between x1 and y1 to the correlation between x2 and y2. The default correlation is the percentage bend correlation. This function is inappropriate to make inferences about Pearson's correlations. Missing values are automatically removed.

Usage

twocorci.nov(
  x1,
  y1,
  x2,
  y2,
  method = "pbcor",
  nboot = 2000,
  alpha = 0.05,
  alternative = "two.sided",
  null.value = 0,
  saveboot = TRUE,
  ...
)

Arguments

x1, y1, x2, y2	Four dependent vectors of the same length.
method	A function that returns a correlation coefficient. Options include "spearman", "pbcor", "wincor". Default is "pbcor".
nboot	Number of bootstrap samples. Default 2000.
alpha	Alpha level. Default 0.05. For corfun = pearson, alpha is restricted to 0.05 because the confidence interval adjustments have not been calculated for other alphas.
alternative	Type of test, either "two.sided" (default), "greater" for positive correlations, or "less" for negative correlations.
null.value	Hypothesis to test. Default 0.
saveboot	Option to save bootstrap samples. Default TRUE.
...	Optional parameter to pass to correlation function.

Value

• estimate.x1y1 the correlation coefficient between x1 and y1.

• estimate.x2y2 the correlation coefficient between x2 and y2.

• difference the difference between correlation coefficients.

• conf.int the bootstrap confidence interval.

• p.value the bootstrap p value.

• bootsamples the bootstrap samples.

Note

Modified from function twoDNOV from Rallfun-v37.txt - see https://github.com/nicebread/WRS/ and http://dornsife.usc.edu/labs/rwilcox/software/.

References

Wilcox, R.R. (2016) Comparing dependent robust correlations. Br J Math Stat Psychol, 69, 215–224.

Wilcox, R.R. (2017) Introduction to Robust Estimation and Hypothesis Testing, 4th edition. Academic Press.

GRousselet/bootcorci documentation built on March 6, 2021, 7:13 a.m.