twocorci: Compare two independent correlations

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 independent groups. The default correlation is the percentage bend. When using Pearson's correlation, the confidence interval is adjusted to compensate for the error term's heteroscedasticity. Missing values are automatically removed.

Usage

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

Arguments

x1, y1	Two dependent vectors of the same length from group 1.
x2, y2	Two dependent vectors of the same length from group 2.
method	A function that returns a correlation coefficient. Options in bootcorci include "pearson", "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

• estimate1 the correlation coefficient for group 1.

• estimate2 the correlation coefficient for group 2.

• 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 functions twocor and twopcor from Rallfun-v37.txt - see https://github.com/nicebread/WRS/ and http://dornsife.usc.edu/labs/rwilcox/software/.

References

Wilcox, R.R. (2009) Comparing Pearson Correlations: Dealing with Heteroscedasticity and Nonnormality. Communications in Statistics - Simulation and Computation, 38, 2220–2234.

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.