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Transient I V curves for each set of parameter data points were determined and the maximum power generation, fill factor and efficiency of the system calculated for each individual I V curve.
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For one set of parameter settings, the data set contains intensity values for all detected retention time/mass pairs of the 20 samples.
For this latter set of parameters, the data did not allow for the estimation of inter-individual variation.
Therefore, based on current literature, we defined a priori a set of parameters for data collection regarding DMC characteristics, interim analysis and early termination.
The challenge would then be to test out these models using the same set of parameter values on new data when available.
To address this problem we use a parameter ensemble approach, where we for each model extract a number of parameter sets, chosen for the ability of the model (simulated with such a set of parameter values) to explain data from multiple mutant experiments [ 25].
The OFV, expressed for convenience as minus twice the log of the likelihood, is a single number that provides an overall summary of how closely the model predictions (given a set of parameter values) match the data (maximum likelihood = lowest OFV = best fit).
The first set of initial and parameter data refers to normal conditions of life, and so normal starting values.
SNMNMF was also run with the optimized set of parameters and input data described in Zhang et al. (2011).
Thus, we defined a common set of parameters, containing only data that all 3 registries could deliver and consensus was made according to definition of several variables.
In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data.
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