Simulate a realization of a location dependent marked point process

Usage

simulate_mpp(
  process = c("self_correcting"),
  process_fit = NULL,
  t_min = 0,
  t_max = 1,
  anchor_point = NULL,
  raster_list = NULL,
  scaled_rasters = FALSE,
  mark_model = NULL,
  xy_bounds = NULL,
  include_comp_inds = FALSE,
  competition_radius = 15,
  edge_correction = "none",
  thinning = TRUE,
  seed = NULL
)

Arguments

process: type of process used (currently supports "self_correcting").
process_fit: either (1) a ldmppr_fit object returned by estimate_process_parameters, or (2) a numeric vector of length 8 giving self-correcting parameters: $(\alpha_1,\beta_1,\gamma_1,\alpha_2,\beta_2,\alpha_3,\beta_3,\gamma_3)$ (alpha_1, beta_1, gamma_1, alpha_2, beta_2, alpha_3, beta_3, gamma_3).
t_min: minimum value for time.
t_max: maximum value for time.
anchor_point: (optional) vector of (x,y) coordinates of the point to condition on. If NULL, inferred from the reference data (largest mark if available) or from process_fit$data_original (largest size).
raster_list: a list of raster objects used for predicting marks.
scaled_rasters: TRUE or FALSE indicating whether the rasters have already been scaled.
mark_model: a mark model object. May be a ldmppr_mark_model or a legacy model.
xy_bounds: (optional) vector of bounds as c(a_x, b_x, a_y, b_y). If NULL, will be inferred from reference_data's window when reference_data is provided, otherwise from ldmppr_fit with lower bounds assumed to be 0.
include_comp_inds: TRUE or FALSE indicating whether to compute competition indices.
competition_radius: distance for competition radius if include_comp_inds = TRUE.
edge_correction: type of edge correction to apply ("none" or "toroidal").
thinning: TRUE or FALSE indicating whether to use the thinned simulated values.
seed: integer seed for reproducibility.

Value

an object of class "ldmppr_sim".

Examples

# Specify the generating parameters of the self-correcting process
generating_parameters <- c(2, 8, .02, 2.5, 3, 1, 2.5, .2)

# Specify an anchor point
M_n <- c(10, 14)

# Load the raster files
raster_paths <- list.files(system.file("extdata", package = "ldmppr"),
  pattern = "\\.tif$", full.names = TRUE
)
raster_paths <- raster_paths[!grepl("_med\\.tif$", raster_paths)]
rasters <- lapply(raster_paths, terra::rast)

# Scale the rasters
scaled_raster_list <- scale_rasters(rasters)

# Load the example mark model
file_path <- system.file("extdata", "example_mark_model.rds", package = "ldmppr")
mark_model <- load_mark_model(file_path)

# Simulate a realization
example_mpp <- simulate_mpp(
  process = "self_correcting",
  process_fit = generating_parameters,
  t_min = 0,
  t_max = 1,
  anchor_point = M_n,
  raster_list = scaled_raster_list,
  scaled_rasters = TRUE,
  mark_model = mark_model,
  xy_bounds = c(0, 25, 0, 25),
  include_comp_inds = TRUE,
  competition_radius = 10,
  edge_correction = "none",
  thinning = TRUE
)

# Plot the realization and provide a summary
plot(example_mpp, pattern_type = "simulated")

summary(example_mpp)
#>                    Length Class      Mode     
#> process             1     -none-     character
#> mpp                 6     ppp        list     
#> realization         4     data.frame list     
#> params              8     -none-     numeric  
#> bounds              3     -none-     list     
#> anchor_point        2     -none-     numeric  
#> thinning            1     -none-     logical  
#> edge_correction     1     -none-     character
#> include_comp_inds   1     -none-     logical  
#> competition_radius  1     -none-     numeric  
#> call               14     -none-     call     
#> meta                2     -none-     list