diffcor (version 0.8.4)

bootcor.dep: Bootstrapped Correlation Difference Test for Dependent Correlations

Description

Derivation of bootstrap confidence intervals for the calculation of correlation differences for dependent correlations.

Usage

bootcor.dep(target,
            x1,
            x2,
            k = 5000,
            alpha = .05,
            digit = 3,
            seed = 1234)

Value

r_target_1

The empircally observed correlation between the first variable and the target variable.

r_target_2

The empircally observed correlation between the second variable and the target variable.

M

Mean of the confidence interval of the correlation difference between r_target_1 and r_target_2.

LL

Lower limit of the confidence interval of the correlation difference between r_target_1 and r_target_2, given the entered Type I-level.

UL

Upper limit of the confidence interval of the correlation difference between r_target_1 and r_target_2, given the entered Type I-level.

Arguments

target

A vector containing the values for the target variable for which the correlations of the two competing variables x1 and x2 should be compared.

x1

A vector containing the values of the first variable being correlated with the target variable.

x2

A vector containing the values of the second variable being correlated with the target variable.

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.dep() 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 correlations of two competing variables with a target variable, 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

df <- data.frame(target = rnorm(1000),
                 var1 = rnorm(1000),
                 var2 = rnorm(1000))

bootcor.dep(target = df$target,
            x1 = df$var1,
            x2 = df$var2,
            k = 5000,
            alpha = .05,
            digit = 3,
            seed = 1234)