The basic functionality of the software that loads, links, plots and fits model and data will be explained.
In the folder
/Examples we provide some showcase applications that can be used as a starting point for new modeling projects.
Once models and data have been implemented in the current working directory (in subfolders
/data) models and data can be loaded, compiled and linked. A typical MATLAB script would look like this
arInit; arLoadModel('model1', 1); arLoadModel('model2', 2); arLoadData('data1_for_model1', 1); arLoadData('data2_for_model1', 1); arLoadData('data3_for_model2', 2); arLoadData('data4_for_model2', 2); arCompileAll;
After the setup script finished successfully full functionality can be used. All information about model and data are now available in the global variable
ar. Below some basic function are explained briefly.
Basic commands / functions
Displays the list of all model parameters, their current values, upper bound (ub) and lower bound (lb), ... in the MATLAB command window.
Parameters: # = free, C = constant, D = dynamic, I = initial value, E = error model name lb value ub 10^value fitted prior # 1|DI | CISEqc | -3 +2.6 +4 | 1 +4.3e+02 | 1 | uniform(-3,4) # 2|DI | CISEqcOE | -3 -0.28 +3 | 1 +0.53 | 1 | uniform(-3,3) # 3|D | CISInh | -3 +8.9 +12 | 1 +7.9e+08 | 1 | uniform(-3,12) ...
Opens plots of the model trajectories and of the experimental data for the current parameter values.
arPlotter opens a small graphical user interface that allows select with data sets and available quantities to display by
Runs the currently selected numerical optimization algorithm to calibrate the model to the experimental data. Maximum likelihood estimation is used here. If the the option flag in the global variable
ar.config.showFitting = true the process of the model calibration is shown during the estimation (see
Opens a small graphical user interface that allows to manually adjust parameter values and updates the corresponding plots (see
Executes a sequence of
n fits (see
arFit). For deterministic optimization the initial parameter guesses are generated by Latin hypercube sampling (LHS). The results of the
n fits are displayed by
arPlotFits sorted by goodness of fit and the parameter values of the best fit replace the previous values.