Correlations were significant, at r = −0.896 and r = −0.917, respectively, at the significance level of 0.01 using multivariate correlation analysis based on Pearson's correlation coefficient (Table 2).
Comparative analysis was conducted using a multivariate correlation-based visualization application developed in-house.
This analysis involved a multivariate correlation-based visualization application [ 24] that has been used to compare two independent gene expression profile datasets [ 11, 25].
RF is a multivariate correlation-based feature selection method that compares feature values of the k nearest instances for the same and the other classes [ 37].
The distance correlation based sensitivity index not only considers the whole probability distribution of multivariate output but also can be easily estimated with only a single set of input output samples.
There are several statistical models applicable for multivariate correlation scoring (instead of two-class based scoring).
It is evident that this problem can not be solved by simple linear multivariate correlation.
§ Pearsons correlation used for univariate correlation estimations and §§ partial rho correlations used for multivariate correlation estimations.
PCA can be viewed as a multivariate correlation analysis.
We used Pearson's linear correlation with CI 95% and a multivariate correlation.
