update.ca_model: Update a cellular automaton

Description

Update the definition of a stochastic cellular automaton (SCA), using new parameters, type of wrapping, or any other parameters entering in the definition of the model.

Usage

# S3 method for ca_model
update(
  object,
  parms = NULL,
  neighbors = NULL,
  wrap = NULL,
  fixed_neighborhood = NULL,
  check_model = "quick",
  verbose = FALSE,
  ...
)

Value

This function returns a list with class ca_model with the changes applied to the original model (see camodel for details about this type of object).

Arguments

object: The SCA object (returned by camodel)
parms: a named list of parameters, which should be all numeric, single values. If this list contains only a subset of model parameters, the old parameter values will be reused for those not provided.
neighbors: The number of neighbors to use in the cellular automaton ('4' for 4-way or von-Neumann neghborhood, or '8' for an 8-way or Moore neighborhood)
wrap: If TRUE, then the 2D grid on which the model is run wraps around at the edges (the top/leftmost cells will be considered neighbors of the bottom/rightmost cells)
fixed_neighborhood: When not using wrapping around the edges (wrap = TRUE, the number of neighbors per cell is variable, which can slow down the simulation. Set this option to TRUE to consider that the number of neighbors is always four or eight, regardless of the position of the cell in the landscape, at the cost of approximate dynamics on the edge of the landscape.
check_model: A check of the model definition is done to make sure there are no issues with it (e.g. probabilities outside the [1,0] interval, or an unsupported model definition). A quick check that should catch most problems is performed if check_model is "quick", an extensive check that tests all neighborhood configurations is done with "full", and no check is performed with "none".
verbose: whether information should be printed when parsing the model definition.
...: extra arguments are ignored

Details

This function updates some aspects of a pre-defined stochastic celullar automaton, such as parameter values, the type of neighborhood, whether to wrap around the edge of space, etc. It is handy when running multiple simulations, and only a few aspects of the model needs to be changed, such as parameter values. Note that this function cannot add or remove states to a model.

Note that the parms list may only specify a subset of the model parameters to update. In this case, old parameter values not specified in the call to update will be re-used.

Examples

Run this code



# Update the parameters of a model
mussels <- ca_library("musselbed")
mussels[["parms"]] # old parameters
mussels_new <- update(mussels, parms = list(d = 0.2, delta = 0.1, r = 0.8))
mussels_new[["parms"]] # updated parameters

# Update the type of neighborhood, wrapping around the edges, and
# the parameters
mussels_new <- update(mussels,
                      parms = list(d = 0.2, delta = 0.1, r = 0.8),
                      wrap = TRUE,
                      neighbors = 8)
mussels_new

# \donttest{

# Run the model for different values of d, the death rate of mussels
ds <- seq(0, 0.25, length.out = 12)
initmat <- generate_initmat(mussels, c(0.5, 0.5, 0), nrow = 64, ncol = 64)
results <- lapply(ds, function(this_dvalue) {
  musselmod <- update(mussels, parms = list(d = this_dvalue))
  run <- run_camodel(musselmod, initmat, times = seq(0, 128))
  data.frame(d = this_dvalue,
             as.data.frame(tail(run[["output"]][["covers"]], 1)))
})

results <- do.call(rbind, results)
plot(results[ ,"d"], results[ ,"MUSSEL"], type = "b",
     xlab = "d", ylab = "Mussel cover")

# }