diffcor (version 0.8.4)

bootcor.one: Bootstrapped Correlation Difference Test between an Empirical and an Expected Correlation

Description

Derivation of bootstrap confidence intervals for the calculation of correlation differences between the empirically observed correlation coefficient and a threshold against which this coefficient is tested.

Usage

bootcor.one(x,
            y,
            r_target,
            k = 5000,
            alpha = .05,
            digit = 3,
            seed = 1234)

Value

r_emp

The empircally observed correlation between x and y.

r_target

The threshold against which r_emp is tested.

M

Mean of the confidence interval of the correlation difference between r_emp and r_target.

LL

Lower limit of the confidence interval of the correlation difference between r_emp and r_target, given the entered Type I-level.

UL

Upper limit of the confidence interval of the correlation difference between r_emp and r_target, given the entered Type I-level.

Arguments

x

A vector containing the values of the first variable being involved in the correlation.

y

A vector containing the values of the second variable being involved in the correlation.

r_target

A single value against which the correlation between x and y is tested.

k

The number of bootstrap samples to be drawn. The default is 5000.

alpha

Likelihood of Type I error. The default is .05.

digit

Number of digits in the output. The default is 3.

seed

A random seed to make the results reproducible.

Author

Christian Blötner

Details

Bivariate correlation analyses as well as correlation difference tests possess very strict statistical requirements that are not necessarily fulfilled when using the basic diffcor.one() function from this package (Wilcox, 2013 <doi:10.1016/C2010-0-67044-1>). For instance, if the assumption of a normal distribution does not hold, the significance test can lead to false positive or false negative conclusions. To address potential deviations from normal distribution, the present function applies bootstrapping to the data. The output provides a confidence interval for the difference between the empirically observed correlation coefficient and the threshold against which this coefficient should be tested, whereby the interval is derived from bootstrapping samples.

References

Wilcox, R. (2013). Introduction to robust estimation and hypothesis testing. Elsevier. https://doi.org/10.1016/C2010-0-67044-1

Examples

df <- data.frame(a = rnorm(1000),
                 b = rnorm(1000))

bootcor.one(x = df$a,
            y = df$b,
            r_target = .10,
            k = 5000,
            alpha = .05,
            digit = 3,
            seed = 1234)