TWO-SAMPLE CORRGRAMS
Visualizing Dichotomous Data Correlations Using Two-Sample Corrgrams
Welcome to our website created for the 2021 Sigma Xi Student Research Showcase and Conference! We are the sister and brother duo, Rithika and Rohan Reddy Tummala, and we hope you enjoy exploring two-sample corrgrams, a novel graphical representation we created to efficiently present multivariate correlations for data sets with two samples. Our R package, "corrarray," streamlines the generation of your own two-sample correlation matrices and corrgrams: www.bit.ly/corrarray.
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Rohan Reddy Tummala
M.D. Candidate
University of Tennessee Health Science Center
Memphis, TN
Rithika Reddy Tummala
B.A. Candidate
Vanderbilt University
Nashville, TN
ABSTRACT
Corrgrams enable visualization of multivariate correlation matrices for a data set by using heat
maps. Comparing multivariate correlations between two groups of interest using traditional
corrgrams necessitates two separate one-sample corrgrams in order to display the correlation
matrix of each group. Here, we introduce two-sample correlation matrices and corrgrams as an
efficient solution for visualizing multivariate correlations in data sets stratified into dichotomous
groups. We also introduce the R package "corrarray," which streamlines the generation of novel
two-sample correlation matrices. The lower and upper triangular correlation matrices of the first
and second sample, respectively, are displayed on opposite sides of the principal diagonal in a
single correlation matrix. When a data set’s grouping variable has more than two levels, this
package provides functionality to visualize a multi-sample array comprising individual
correlation matrices for k levels of the grouping variable. Visualizing correlation matrices of a
dichotomous data set using a single two-sample corrgram eliminates the redundancy and
inefficient space utilization that would have otherwise resulted from resorting to traditional
corrgrams.
PRESENTATION
POSTER
ARTICLE IN AN ACADEMIC JOURNAL
