diffcor (version 0.8.4)

bootcor.two: Bootstrapped Correlation Difference Test between Correlations from Two Independent Samples

Description

Derivation of bootstrap confidence intervals for the calculation of correlation differences between the empirically observed correlations obtained from two independent samples.

Usage

bootcor.two(x1,
            y1,
            x2,
            y2,
            k = 5000,
            alpha = .05,
            digit = 3,
            seed = 1234)

Value

r1

The empircally observed correlation between x and y in Sample 1.

r2

The empircally observed correlation between x and y in Sample 2.

M

Mean of the confidence interval of the correlation difference between the correlations from the two samples.

LL

Lower limit of the confidence interval of the correlation difference between the correlations from the two samples, given the entered Type I-level.

UL

Upper limit of the confidence interval of the correlation difference between the correlations from the two samples, given the entered Type I-level.

Arguments

x1

A vector containing the values of the first variable being involved in the correlation in Sample 1.

y1

A vector containing the values of the second variable being involved in the correlation in Sample 1.

x2

A vector containing the values of the first variable being involved in the correlation in Sample 2.

y2

A vector containing the values of the second variable being involved in the correlation in Sample 2.

k

The number of bootstrap samples that should 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.two() 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 coefficients obtained from two independent samples, whereby the interval is derived from bootstrapping.

References

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

Examples

df1 <- data.frame(a = rnorm(1000),
                  b = rnorm(1000))

df2 <- data.frame(x = rnorm(600),
                  y = rnorm(600))

bootcor.two(x1 = df1$a,
            y1 = df1$b,
            x2 = df2$x,
            y2 = df2$y,
            k = 5000,
            alpha = .05,
            digit = 3,
            seed = 1234)