TOSTER (version 0.8.3)

z_cor_test: Test for Association/Correlation Between Paired Samples

Description

Test for association between paired samples, using one of Pearson's product moment correlation coefficient, Kendall's \(\tau\) (tau) or Spearman's \(\rho\) (rho). Unlike the stats version of cor.test, this function allows users to set the null to a value other than zero.

Usage

z_cor_test(
  x,
  y,
  alternative = c("two.sided", "less", "greater", "equivalence", "minimal.effect"),
  method = c("pearson", "kendall", "spearman"),
  alpha = 0.05,
  null = 0
)

Value

A list with class "htest" containing the following components:

• "p.value": the p-value of the test.

• "estimate": the estimated measure of association, with name "pb", "wincor", "cor", "tau", or "rho" corresponding to the method employed.

• "null.value": the value of the association measure under the null hypothesis.

• "alternative": character string indicating the alternative hypothesis (the value of the input argument alternative).

• "method": a character string indicating how the association was measured.

• "data.name": a character string giving the names of the data.

• "call": the matched call.

Arguments

x, y

numeric vectors of data values. x and y must have the same length.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater", "less", "equivalence" (TOST), or "minimal.effect" (TOST). You can specify just the initial letter.

method

a character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.

alpha

alpha level (default = 0.05)

null

a number indicating the null hypothesis. Default is a correlation of zero.

Details

This function uses Fisher's z transformation for the correlations, but uses Fieller's correction of the standard error for Spearman's \(\rho\) and Kendall's \(\tau\). See vignette("correlations") for more details.

References

Goertzen, J. R., & Cribbie, R. A. (2010). Detecting a lack of association: An equivalence testing approach. British Journal of Mathematical and Statistical Psychology, 63(3), 527-537. https://doi.org/10.1348/000711009X475853, formula page 531.

See Also

Other Correlations: boot_cor_test(), corsum_test(), plot_cor(), power_z_cor()

Examples

# example code
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)
# Sig test
z_cor_test(x, y, method = "kendall", alternative = "t", null = 0)
# MET test
z_cor_test(x, y, method = "kendall", alternative = "min", null = .2)