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Field measurements of plant growth and architecture were used for parameterization; the subset of parameters that cannot be obtained by field estimation were estimated by fitting model predictions to a fine-scale time series of field data using a formalized gradient-descent procedure.
A microstructure-based crystal plasticity model that accounts for non-Schmid behavior in the ferrite phase and contains a detailed description of the hierarchical microstructure of martensite was developed and material parameters were determined by fitting model predictions to the micropillar compression data.
Only a few parameters were later determined by fitting model predictions to clinical data.
Parameters (Table 1) were also drawn from publications or estimated by fitting model predictions to trial outcomes.
The model structure results in a set of ordinary differential equations (ODE) and model parameters are often determined by fitting model predictions to the experimental data.
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Diffusion coefficient parameter in the models was obtained by fitting the model predictions with the experimental data.
To estimate λ we developed a three age-class (newborns, juveniles and adults) female-only matrix model, and estimated the matrix parameters by fitting the model predictions to the field total population estimates; we summed the three age classes and multiplied the result by two (guanacos have an approximately 50% sex-ratio).
The parameter estimation of a nonlinear kinetic empirical model representing this system is made via genetic algorithms by fitting the model predictions against our laboratory observations.
The two parameters were obtained by fitting the model predictions to the experimental responses and were found to obey the van't Hoff relation.
10 fold cross validation was performed by fitting linear regression models to the data, excluding 1/10th of the data in each fold and using the fitted model for prediction on data that was excluded from the fold.
Lines represent predictions by the fitted model (equation 1).
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