binary variablesIn attempting to represent a multivariable system by a simpler one with only binary variables it is not always possible to achieve the required correlations. This paper explores the extra constr
Star graphs induce tetrad correlations: for Gaussian as well as for binary variables. Electr. J. Statist. 8, 253-273.Wermuth, N. & Marchetti, G.M (2014). Star graphs induce tetrad correlations: for Gaussian as well as for binary variables. Electronic Jornal of Statistics 8, 253-273....
Computes an tetrachoric correlation matrix for binary variables given the specified correlation matrixn.BB
This has led to the creation of an empirical test to determine the appropriateness of using tetrachoric correlations for binary variables (Muthén and Hofacker, 1988). The tetrachoric correlation has also been found to be a biased estimated of the population correlation, with low expected cell ...
We compare an algorithm based on dichotomizing multi-normal variates to one based on a conditional linear family (CLF) of distributions [Qaqish BF. A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations. Biometrika. 2003;...
Phi Coefficient measures the association between two binary variables, such as living/dead, black/white, or success/failure. It is also known as the Yule phi or Mean Square Contingency Coefficient. This statistical measure is used for contingency tables when at least one variable is nominal and ...
Lunn and Davies (1998) proposed linear time complexity methods to generate correlated binary variables with three common correlation structures. However, it is infeasible to specify unequal probabilities in their methods. In this manuscript, we introduce several computationally efficient algorithms that ...
We show that the functional organization of networks with latent binary interface variables can be inferred from the statistical moments of observable network components alone, and derive a statistical test for hierarchical modularizations. Importantly, this test can also be applied to refute functional ...
I ran biserial correlations between continuous and binary variables in SPSS on a large dataset of 762 patients. I correlated 138 regional brain volumes with 7 binary cognitive outcomes to explore the association between regional brain tissue volumes and cognitive impairment. ...
Data Structure: CCA requires two sets of variables, often referred to as "X-variables" and "Y-variables." Each set can contain multiple variables, making CCA suitable for examining complex relationships. Objective: The primary objective of CCA is to find linear combinations of X-variables and ...