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Since a single fitting index reflects only a particular aspect of model fit, a favorable value of that index does not by itself demonstrate good model fit; it is important to assess the model fit based on multiple indices.
Our model p-value based LEO.NB score can be considered as a relative fitting index that contrasts the fit of the causal orientation to that of the other models.
We calculated the LEO.NB.SingleMarker (LEO) score, which is a relative fitting index that compares the model fitting p-value of the causal model for a gene x i causing ME to that of the next best competing model.
A potential use of NEO is to use it for initializing an iterative edge orienting algorithms for large networks that maximizes a global SEM fitting index.
As described in the next step, all fitting indices should be considered before calling an edge causal.
To measure how well a network satisfies a scale-free topology, we use the fitting index proposed by Zhang & Horvath [30], i.e., the model fitting index R2 of the linear model that regresses log(p k)) on log(k) where k is connectivity and p(k) is the frequency distribution of connectivity.
To measure how well a network satisfies a scale-free topology, we use the fitting index proposed by Zhang & Horvath [ 71] (i.e., the model fitting index R 2 of the linear model that regresses log(p k)) on log(k) where k is connectivity and p(k) is the frequency distribution of connectivity).
The fitting index of a perfect scale-free network is 1.
First, use each fitting index to make an overall evaluation of the model.
Alternative edge orienting scores could be defined by replacing the model p-value by another fitting index for which high values indicate good fit, e.g. the comparative fitting index (CFI).
For this dataset, we select the smallest β ( = 6) which leads to an approximately scale-free network with the truncated scale-free fitting index R2 greater than 0.8.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com