The statistically significant diagonal values in the Cholesky matrix highlight correlation between attributes making an uncorrelated specification inappropriate (matrices for models with correlation available in Appendix A).
The next generation matrix for model (2.1) is FV^{-1}= begin{matrix} {frac{beta h(S_{0})(b+gamma+mu+varepsilon)}{(gamma+b+mu )(c+varepsilon+mu)}} & {frac{beta h(S_{0})}{gamma+b+mu}} 0 & 0 end{matrix}.
This study demonstrates methods for recovering a covariance matrix for model parameters in a biomass equation from fit statistics restricted to: sample size (n) and the coefficient of determination (R2) [44].
This study demonstrates methods for recovering a covariance matrix for model parameters in a biomass equation from fit statistics restricted to: sample size (n) and the coefficient of determination (R) [ 44].
The correlation matrix for Model 2 leads to many yellow squares outside of the diagonal, corresponding to highly correlating sites in the regulatory regions of different genes due to indirect interactions.
The prior distribution of the parameter vector (β 0, i [ k ], β [ k ], σ 2 [ k ] ) of model M k is β [ k ] | σ 2 [ k ], M k, g ∼ N (0, g σ 2 [ k ] (X k T X k ) - 1 ), p (β 0, i [ k ], σ 2 [ k ] | M k ) ∝ 1 / σ 2 [ k ], where X k is the design matrix for model M k and g > 0 controls the prior variance of the regression parameters.
Let R be the working correlation matrix for modeling the within-group correlation, a square max{ n i } × max{ n i } matrix.
Second, we conducted a Monte Carlo based uncertainty analysis to determine the robustness of our results to ±5% error introduced into the original flow matrices for each model.
An alternative solution is to rearrange the tensor as a matrix using the unfolding operation and then utilize the eigenvalues of the resultant matrices for CP model order selection.
The correlation matrices for all model structures (Additional file 1: Figures S7, S9 and S11) show high absolute correlation values for certain sets of parameters.
