Turing function has been designed to evaluate an Operating Model against a Data object from the same fishery. The function generates 5 random samples of Data from the
OM object and plots these together with the corresponding data in the
Ideally, in a well conditioned OM one should not be able to visually detect which of the plots are the real data and which have been artificially generated by the operating model.
Turing function takes an object of class
OM and an object of class
Data. It first plots the simulated and real data and then waits for user input before revelaing which of the plots are the real data from the
We use the
wait=FALSE argument here so that each plot is printed without waiting for user input.
In this example we are using a
Data object that was simulated using the same
OM, so it shouldn’t be surprising that it is difficult to detect which of the plots are from the
Data object (we haven’t included the plots here):
Turing function is useful for evaluating if your OM adequately produces fishery data that appears similar (e.g as variable) as your real observed data. See
?Turing or function help documentation for more information.