knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.height = 6, fig.width = 8 )
This vignette outlines the tools provided by growR
for model calibration.
It focuses on the practical aspects of which functions to call when.
For an in-depth discussion about the calibration procedure as a whole, see
the respective article{target="_blank"}.
We cannot calibrate anything without some reference data for which we want to
identify ideal model parameters.
For the purpose of this tutorial, we are going to make use of the example
data provided with the package.
If you use your own data, be sure that it adheres to the data file format as
outlined in load_measured_data()
.
Furthermore, we need everything that's also required to run a simulation: weather input, a parameter file and, optionally, management data. Confer the introductory tutorial for details. Again, we are going to use the example data here (though you can feel free to use your own). We'll use the same setup as in the introductory tutorial:
# Store the original wd original_dir = getwd()
working_dir = file.path(tempdir(), "growR_calibration_tutorial") dir.create(working_dir) setwd(working_dir) library(growR) setup_directory(working_dir, force = TRUE)
# knitr resets working dir to root.dir after every chunk. # This makes sure we stay in working_dir knitr::opts_knit$set( root.dir = working_dir )
We will also already load the example configuration. For the sake of saving on computation time in this example, we limit the simulation to just one year. In a real-world situation, you should of course make use of all the calibration data available to you!
envs = read_config("example_config.txt") # We only need one run environment. env = envs[[1]] # Save compuation time by considering only one year env$years = env$years[1]
The essential approach to finding optimized parameters for a given site is to run and evaluate the model for many different sets of parameters. By comparing their performance scores, we can hone in on those parameter combinations that are promising. We then evaluate the model with more parameter sets, now in the regions of parameter space where we suspect good performace. We can keep doing this until we do not seem to find better parameter sets anymore or until we are satisfied (see the in-depth article{target="_blank"} for a much more detailed description).
The three functions that facilitate this iterative procedure are
run_parameter_scan()
, analyze_parameter_scan()
and plot_parameter_scan()
.
The first makes it easy to carry out model runs for many different parameter
sets.
analyze_parameter_scan()
then takes the output of these model runs and
compares them to a set of reference data to assign performance scores to each run.
Finally, plot_parameter_scan()
visualizes these performance scores as a
function of input parameters and thus helps identify good regions in
parameter space.
We can define the parameter values of interest as input to
run_parameter_scan()
.
The function will then automatically generate all possible, valid
combinations of parameters and run the model in a given configuration for
each of these parameter combinations.
Given that even for relatively small parameter ranges we can end up with a
number of combinations in the order of 100, this step can become quite
calculation intensive.
Initially, if we have absolutely no prior knowledge about the parameters, we
have to consider all parameters across their full possible ranges.
An input for run_parameter_scan()
might thus look like this:
param_values = list(w_FGA = seq(0, 1, 0.25), w_FGB = seq(0, 1, 0.25), w_FGC = seq(0, 1, 0.25), w_FGD = seq(0, 1, 0.25), NI = seq(0.25, 1, 0.25))
Be sure to read the documentation of run_parameter_scan()
and the more
detailed descriptions in create_combinations()
regarding sensible
construction of param_values
.
We are now ready to go:
pscan_results = run_parameter_scan(env, param_values, outfilename = "pscan_results0.rds")
This might take some time to run and will store the results in a binary
format in "pscan_results0.rds"
as well as in the variable pscan_results
.
In order to evaluate the model outputs, we need to compare them to some
reference data.
In our current setup, we know that the corresponding data is stored under
data/
.
# Prepare path to reference data site = env$site_name print(site) datafile = file.path("data", sprintf("%s.csv", site))
Instead of specifying the path we could also have loaded the data and passed
the data.frame as an argument to analyze_parameter_scan()
.
Similarly, we can use the path to the binary file created by our previous
call to run_parameter_scan()
or just the stored object in the variable.
# Analyze! analyzed = analyze_parameter_scan(pscan_results, datafile)
This shouldn't take long to compute. You can have a look at the output data.frame and see how well different combinations perform:
analyzed$results
A much more intuitive way, however, is to visualize these results:
plot_parameter_scan(analyzed)
This should create a plot similar to the following:
We have a subplot for each combination of scanned parameter and used metric (bias, mean absolute error MAE and root of mean-squared error RMSE). In each subplot, we have a point for every evaluated parameter combination.
plot_parameter_scan()
puts you into a small interactive command line
interface (CLI), which allows you to highlight different parameter
combinations in the plot and display their numeric values on the console.
Refer to the on-line help (?
) for more on how to use this little tool.
From this plot we can already make out some trends:
From this we can pin down the ranges of plausible NI values to larger than 0.3 and smaller than 0.9.
From this we could for example narrow down w_FGA to be larger than 0.3, B to be no greater than 0.8 and C and D to be smaller than 0.5.
We can now do a new parameter scan with updated param_values, e.g.
param_values = list(w_FGA = seq(0.3, 1, 0.1), w_FGB = seq(0, 0.7, 0.1), w_FGC = seq(0, 0.3, 0.1), w_FGD = seq(0, 0.3, 0.1), NI = seq(0.5, 0.7, 0.05))
Since we have narrowed down the ranges, we can afford to increase the resolution, i.e. decrease the step size for the parameters. Keep iterating this process until you've narrowed down the parameter ranges to a degree that is acceptable for you.
The exact choice of which parameters ranges to examine is, of course, up to you and might require some experimentation. It might, for example, make sense to keep the functional group weights unconstrained and first pin down NI. In other situations you might be able to make use of some prior knowledge, e.g. a measurement of the plant composition, which gives you a hint towards which functional groups should be prevalent. Refer to the in-depth guide{target="_blank"} for a more rigorous discussion.
# Clean up knitr::opts_knit$set( root.dir = original_dir ) setwd(original_dir) #unlink(working_dir, recursive = TRUE)
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