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Convenience Functions for Partial Application

partial_application.Rd

Convenience functions for using partial application with BioCro

Usage

partial_run_biocro(
    initial_values = list(),
    parameters = list(),
    drivers,
    direct_module_names = list(),
    differential_module_names = list(),
    ode_solver = BioCro::default_ode_solvers$homemade_euler,
    arg_names,
    verbose = FALSE
)

partial_evaluate_module(module_name, input_quantities, arg_names)

Arguments

arg_names

A vector of strings specifying input quantities whose values should not be fixed when using partial application.

initial_values

Identical to the corresponding argument from run_biocro.

parameters

Identical to the corresponding argument from run_biocro.

drivers

Identical to the corresponding argument from run_biocro.

direct_module_names

Identical to the corresponding argument from run_biocro.

differential_module_names

Identical to the corresponding argument from run_biocro.

ode_solver

Identical to the corresponding argument from run_biocro.

verbose

Identical to the corresponding argument from run_biocro.

module_name

Identical to the corresponding argument from evaluate_module.

input_quantities

A list of named numeric elements representing any input quantities required by the module that are not included in arg_names; any extraneous quantities will be ignored by the module.

Details

Partial application is the technique of fixing some of the input arguments to a function, producing a new function with fewer inputs. In the context of BioCro, partial application can often be useful while varying some parameters, initial values, or drivers while performing optimization or sensitivity analysis. Optimizers (such as optim) typically require a function with a single input argument, so the partial application tools provided here help to create such functions.

Both partial_run_biocro and partial_evaluate_module accept the same arguments as their "regular" counterparts (run_biocro and evaluate_module) with the addition of arg_names, which specifies the input quantities that should not be fixed.

For partial_run_biocro, each element of arg_names must be the name of a quantity that is one of the initial_values, parameters, or drivers. For partial_evaluate_module, each element of arg_names must be the name of one of the module's input quantities.

When using one of the pre-defined crop growth models, it may be helpful to use the with command to pass arguments to partial_run_biocro; see the documentation for crop_model_definitions for more information.

Value

partial_run_biocro

A function that calls run_biocro with all of the inputs (except those specified in arg_names) set to the values specified by the original call to partial_run_biocro. The new function has one input (x), which can be a vector or list specifying the values of the quantities in arg_names. If x has no names, its elements must be supplied in the same order as in the original arg_names. If x has names, they must be identical to the elements of arg_names but can be in any order. Elements of x corresponding to drivers must be vectors having the same length as the other drivers; they can be specified as a named element of a list or as sequential elements of a vector without names. The return value of the new function is a data frame as would be produced by run_biocro.

partial_evaluate_module

A function that calls evaluate_module with the input quantities (except those specified in arg_names) set to the values specified by the original call to partial_evaluate_module. The new function has one input (x), which can be a vector or list specifying the values of the quantities in arg_names. If x has no names, its elements must be supplied in the same order as in the original arg_names. If x has names, they must be identical to the elements of arg_names but can be in any order. The return value of the new function is a list with two elements (inputs and outputs), each of which is a list of named numeric elements representing the module's input and output values. (Note that this differs from the output of evaluate_module, which only returns the outputs.)

See also

Examples

# Specify weather data to use in these examples
ex_weather <- get_growing_season_climate(weather$'2005')

# Example 1: varying the thermal time values at which senescence starts for
# different organs in a simulation; here we set them to the following values
# instead of the defaults:
#  - seneLeaf: 2000 degrees C * day
#  - seneStem: 2100 degrees C * day
#  - seneRoot: 2200 degrees C * day
#  - seneRhizome: 2300 degrees C * day
senescence_simulation <- partial_run_biocro(
  miscanthus_x_giganteus$initial_values,
  miscanthus_x_giganteus$parameters,
  ex_weather,
  miscanthus_x_giganteus$direct_modules,
  miscanthus_x_giganteus$differential_modules,
  miscanthus_x_giganteus$ode_solver,
  c('seneLeaf', 'seneStem', 'seneRoot', 'seneRhizome')
)
senescence_result <- senescence_simulation(c(2000, 2100, 2200, 2300))

# Example 2: a crude method for simulating the effects of climate change; here
# we increase the atmospheric CO2 concentration to 500 ppm and the temperature
# by 2 degrees C relative to 2005 temperatures. The commands below that call
# `temperature_simulation` all produce the same result.
temperature_simulation <- partial_run_biocro(
  miscanthus_x_giganteus$initial_values,
  miscanthus_x_giganteus$parameters,
  ex_weather,
  miscanthus_x_giganteus$direct_modules,
  miscanthus_x_giganteus$differential_modules,
  miscanthus_x_giganteus$ode_solver,
  c("Catm", "temp")
)
hot_result_1 <- temperature_simulation(c(500, ex_weather$temp + 2.0))
hot_result_2 <- temperature_simulation(list(Catm = 500, temp = ex_weather$temp + 2.0))
hot_result_3 <- temperature_simulation(list(temp = ex_weather$temp + 2.0, Catm = 500))

# Note that these commands will both produce errors:
# hot_result_4 <- temperature_simulation(c(Catm = 500, temp = ex_weather$temp + 2.0))
# hot_result_5 <- temperature_simulation(stats::setNames(
#   c(500, ex_weather$temp + 2.0),
#   c("Catm", rep("temp", length(ex_weather$temp)))
# ))

# Note that this command will produce a strange result where the first
# temperature value will be incorrectly interpreted as a `Catm` value, and the
# `Catm` value will be interpreted as the final temperature value.
# hot_result_6 <- temperature_simulation(c(ex_weather$temp + 2.0, 500))

# Example 3: varying the base and air temperature inputs to the
# 'thermal_time_linear' module from the 'BioCro' module library. The commands
# below that call `thermal_time_rate` all produce the same result.
thermal_time_rate <- partial_evaluate_module(
  'BioCro:thermal_time_linear',
  within(miscanthus_x_giganteus$parameters, {time = 1}),
  c("temp", "tbase")
)
rate_result_1 <- thermal_time_rate(c(25, 10))
rate_result_2 <- thermal_time_rate(c(temp = 25, tbase = 10))
rate_result_3 <- thermal_time_rate(c(tbase = 10, temp = 25))
rate_result_4 <- thermal_time_rate(list(temp = 25, tbase = 10))
rate_result_5 <- thermal_time_rate(list(tbase = 10, temp = 25))