csm_run_sim_group: Run a group of Cropping System Model (CSM) simulations

In csmbuilder: A Collection of Tools for Building Cropping System Models

Run a group of Cropping System Model (CSM) simulations

Description

Run a group of Cropping System Model (CSM) simulations

Usage

csm_run_sim_group(
  model_function,
  y_init,
  t,
  ...,
  method = "euler",
  return_df = TRUE
)

Arguments

model_function	a rendered model produced by csm_render_model()
y_init	a list of vectors of initial values for the model state variables. To use the same initial conditions for all simulation group members, the list should be of length one. Otherwise, the length of the list should correspond to the number of simulation group members.
t	an optional list of vectors of time points for which simulated model outputs are desired. To use the same time points for all simulation group members, the list should be of length one. Otherwise, the length of the list should correspond to the number of simulation group members.
...	additional arguments to pass to model_function for simulation. Each argument should be supplied as a list. To use the same argument value for all simulation group members, the list should be of length one. Otherwise, the length of the list should correspond to the number of simulation group members.
method	numerical integration method to be used. See deSolve::ode() for more details
return_df	a logical value with a default value of TRUE that indicates whether to return output as a data frame (if set to TRUE) or as a list (if set to FALSE).

Value

If return_df is set to TRUE, the function returns a data frame with one row for each time point specified by t within each simulation group member. The simulation group member is indicated by a column within the data frame (sim_no). If return_df is set to FALSE, the function returns a list of data frames, each of which is the output of a simulation group member and includes one row for each time point specified by t

Examples

# Define state variables
lv_state <- csm_create_state(
  c("x", "y"),
  definition = c("prey", "predator"),
  units = c("rabbits per square km", "foxes per square km"),
  expression(~alpha*x-beta*x*y, ~delta*x*y-gamma*y))

# Define parameters
lv_parameters <- csm_create_parameter(
  c("alpha", "beta", "gamma", "delta"),
  definition = c("maximum prey per capita growth rate",
                 "effect of predator population on prey death rate",
                 "predator per capita death rate",
                 "effect of prey population on predator growth rate"),
  units = c("rabbits per rabbit", "per fox",
            "foxes per fox", "foxes per rabbit"))

# Define model
lotka_volterra_model <-
  csm_create_model(
    state = lv_state,
    parms = lv_parameters)

# Render model into a callable R function
lotka_volterra_fun <-
  csm_render_model(lotka_volterra_model,
                   output_type = "function",
                   language = "R")

# Run model simulations for two parameter vectors
lotka_volterra_out <-
  csm_run_sim_group(model_function = lotka_volterra_fun,
              y_init = list(c(x = 10,
                              y = 10)),
              t = list(csm_time_vector(0, 100, 0.01)),
              parms = list(c(alpha = 1.1,
                             beta = 0.4,
                             gamma = 0.1,
                             delta = 0.4),
                           c(alpha = 1.2,
                             beta = 0.5,
                             gamma = 0.15,
                             delta = 0.35)))

csmbuilder documentation built on Sept. 19, 2026, 1:06 a.m.