which is, following the standard definition of the Pearson's correlation coefficient, the covariance between two models divided by the product of their standard deviation.
The difference between two models was estimated by likelihood ratio test statistics, twice the log likelihood difference between the two compared models (2Δl) compared against χ distribution.
The significant difference between two models was evaluated by calculating twice the log-likelihood difference, and followed an χ distribution with the number degree of freedom equal to the difference in the number of free parameters.
Experimental data constraints can be incorporated into the modeling to both to provide better discrimination between two models deemed equally fit by REF and to reduce the search space by excluding vast regions of the search space that are incompatible with the observed data.
We then conducted a multiple group confirmatory factor analysis (CFA) to test for measurement invariance between the two models by comparing the structure on the present sample against the previously published structure.
A similar transition between the two models by Bala and Goyal (2000a) studied in Olaizola and Valenciano (2014) is obtained by assuming: begin{aligned} g_{ij}^{text {BG12}}:=alpha g_{ij}^{min }+ 1-alpha )g_{ij}^{max }, end{aligned}but in this paper we concentrate on (g_{ij}^{text {JWBG1}}) and ( g_{ij}^{text {JWBG2}}) link formation models.
(C – F ) Discriminating between the two models by measuring the physical associations near (<1.5 μm) and far from focal adhesions for 28 protein pairs as named in (E ).
Besides "closing the loop" between the two models by incorporating two-way inter-model signalling (involving Clb5, Cdc20, and FORK), we had to deal with a single shared species: both models describe the dynamics of Cdc6.
The algorithm we present here is useful for measuring the planimetric discrepancy between two models which can be represented by contours.
