visualize_association {CINNA}

Pairwise association plot between centrality measures

Description

This function computes regression between pair of centrality measures to show more details of association among them.

Usage

visualize_association(x, y, scale = TRUE)

Arguments

x

a vector containing a centrality measure as independent variable

y

a vector containing a centrality measure as dependent variable

scale

Whether the centrality values should be scaled or not

Details

This function applies regression analysis on two different centrality values in order to find out the corresponding association between them. Regression analysis is a kind of statitiscal method for approximation the association between variables.It asserts that the value of dependent variable changes when the value of independent variable varies.

Value

The function returns a regression plot and the results of the regression analysis between two centrality measures. The function takes two vectors, 'x' and 'y', which represent the independent and dependent centrality measures, respectively. If the 'scale' parameter is set to 'TRUE', the centrality values will be scaled before performing the regression analysis. The function applies regression analysis to estimate the association between the two centrality measures. It creates a scatter plot of the centrality values, with a regression line representing the relationship between the variables. Additionally, the plot includes confidence intervals around the regression line to show the uncertainty of the estimates. The function returns the regression plot and the computed regression results.

Author(s)

Minoo Ashtiani, Mehdi Mirzaie, Mohieddin Jafari

References

Chambers, J. M. (1992). Statistical Models in S. Wadsworth. Pacific Grove, California. Retrieved from [Link to the reference]

Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic Description of Factorial Models for Analysis of Variance. Applied Statistics, 22(3), 392.