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Two multivariate logistic regression models were constructed.
The 25 compounds were used to compute two multivariate QSAR models and two multivariate discriminant analysis models were computed from both active and inactive compounds.
Two multivariate calibrations and a central composite design were employed for optimization of the system.
Canonical correlation analysis (CCA) is a classical tool that explores the dependency of two multivariate random variables and extracts canonical pairs of highly correlated linear combinations.
In particular, MCR NLR is the more accurate as well as the more robust of the two multivariate methods.
Resting-state connectivity analyses on a separate sample (Study 2, N=91) reveal that two multivariate patterns for pain and rejection encoded in the same set of dACC voxels have distinct functional connectivity patterns with the rest of the brain.
Regarding the two multivariate approaches, the one using only the spectral index as covariate has provided better results in terms of unbiasedness and accuracy.
The cross-validation correlation coefficient (Q2) and the root mean square error (RMSE) were employed to assess the prediction performance of the two multivariate models.
In this article we derive an optimal test for testing the significance of covariance matrices of random-effects of two multivariate mixed-effects linear models.
Partial least squares (PLS1) and principal component regression (PCR) are two multivariate calibration methods that allow simultaneous determination of several analytes in spite of their overlapping spectra.
Normal correlations are the means to quantify the linear relationship between two multivariate random variables.
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