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It is a multivariate correlation method to reduce a number of inter-correlated variables into smaller sets of factors with less or no association; it has been applied to study the clustering of metabolic abnormalities that contribute to the metabolic syndrome [ 18, 20, 24, 25].
This multivariate correlation method is well-suited for revealing underlying patterns or structure among variables showing high degrees of inter-correlation, as in the case of environmental risk variables supposedly influencing health status [ 37].
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However, the result of the multivariate correlation coefficient method from Zhu et al.[ 17] can be shown to be the same as the 'simple' method.
PLS-DA, on the other hand, is a commonly used supervised multivariate method that performs multivariate correlation analysis between the predictor variables (the putative biomarker candidates) and the response variables (e.g., the group variable in case of case control studies).
However, the multivariate correlation analysis should be viewed mainly as a hypothesis generating method, given the sample size, rather than as a complete model of the interactions between the included variables.
To achieve this, one must develop a new method to apply a network theoretical approach to multivariate correlation data.
We used the powerful statistical method (principal component analysis; PCA) to investigate the complex multivariate correlation pattern between the psychophysical tests and different psychological variables.
It is evident that this problem can not be solved by simple linear multivariate correlation.
Benefits and issues of multivariate correlation based calibration are evaluated during one year long measurement campaign.
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).
Correlation analysis was performed using Spearman's rank correlation method.
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