The inversion runs by fitting the model function to the data by optimisation of the model parameters.
This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity.
Overall, the tool takes the initial workflow (e.g. that of Figure1) and security requirements, applies the set of transformations and rules, and then uses the cost model function to rank the valid workflows.
As a model function to describe this relationship the Michaelis-Menten saturation curve (Michaelis and Menten 1913; Bolker 2008) was used, as it appears to describe the data well.
How to obtain a "best fit" of model function to data is always, in a sense, a personal choice.
The analyses were done in R, using the generalized linear model function to compute relative risks and deviances, and the vcovHC function of the sandwich package was used to compute robust standard error estimates for the relative risk estimates.
