The sspm workflow.
The package follows an object oriented design, making use of the S4 class systems. The different classes in the package work together to produce a stepwise workflow.
The sspm workflow.
The first pillar of the package’s design is the concept of
boundary data, the spatial polygons that sets the boundary of the
spatial model. The boundary data is ingested into a
sspm_boundary object with a call to
spm_as_boundary().
The boundary data is then discretized into a
sspm_discrete_boundary object with the
spm_discretize() function, dividing the boundary area into
discrete patches.
The second pillar is the recognition of 3 types of data:
trawl, predictors, and
catch (i.e. harvest). The next step in the workflow is
to ingest the data into sspm_dataset objects via a call to
spm_as_dataset().
The first proper modelling step is to smooth the biomass and
predictors data by combining a sspm_dataset, and a
sspm_discrete_boundary. The user specifies a gam formula
with custom smooth terms (see for more details). The output is still a
sspm_dataset object with a smoothed_data slot
which contains the smoothed predictions for all patches.
Then, catch is integrated into the biomass data by calling
spm_aggregate_catch on the two sspm_dataset
that contains catch and smoothed biomass. Productivity and (both log and
non log) is calculated at this step.
The next step consists in combining all relevant datasets for the
modelling of productivity (i.e. the newly created productivity dataset
and the predictor(s) dataset(s)) with a call to sspm().
Additionally, the user may apply lags to the variables with
spm_lag() and determine the split between testing and
training data with spm_split().
The second modelling step consists in modelling productivity per se. Once again, a gam formula with custom syntax is used (see for more details).
The resulting object contains the model fit. Predictions can be
obtained using the built-in predict() method, and plots
with the plot() method.