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First, an initial set of parameters was obtained for each model type by leaving all parameters free and using the basin hopping algorithm provided by SciPy (Blanco-Silva, 2013) to identify a global error basin.
We further checked data support for assuming the log-normal model type by examining quantile quantile (Q Q) plots to compare quantiles of the empirical distribution of loge(t) for events with normal quantiles, for each event type; a straight line is expected.
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We first use our method on simulated networks using several random graph models and show that it correctly classifies networks by model type even when confounded by varying network density and size.
In the present study, we approximate the interaction by other neurons in a minimal model type of approach by injecting a current, which drives the single (otherwise noninteracting) model neuron.
Least square mean estimates of referral rates for each model type were calculated by setting covariate values equal to their population mean values.
Other useful attributes vary by model type.
A summary of study characteristics can be found in Table 1 (sorted by model type).
Conclusions were unaffected by model type.
Descriptive statistics were generated to describe provider and patient characteristics overall and by model type.
These regressions were then stratified by model type to evaluate the transferability of the results between models.
A list of the ecosystems that have been affected by this model type is presented.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com