Check the fit of an estimated model using global envelope tests

Performs global envelope tests for nonparametric L, F, G, J, E, and V summary functions (spatstat/GET). These tests assess goodness-of-fit of the estimated model relative to a reference marked point pattern. The reference marked point pattern can be supplied directly via reference_data (a marked ppp object), or derived internally from a ldmppr_fit object.

Usage

check_model_fit(
  reference_data = NULL,
  t_min = 0,
  t_max = 1,
  process = c("self_correcting"),
  process_fit = NULL,
  anchor_point = NULL,
  raster_list = NULL,
  scaled_rasters = FALSE,
  mark_model = NULL,
  xy_bounds = NULL,
  include_comp_inds = FALSE,
  thinning = TRUE,
  edge_correction = "none",
  competition_radius = 15,
  n_sim = 2500,
  save_sims = TRUE,
  verbose = TRUE,
  seed = 0
)

Arguments

reference_data: (optional) a marked ppp object for the reference dataset. If NULL, the reference pattern is derived from process_fit when process_fit is an ldmppr_fit and contains data_original (preferred) or data with columns (x,y,size).
t_min: minimum value for time.
t_max: maximum value for time.
process: type of process used (currently supports "self_correcting").
process_fit: either an ldmppr_fit object (from estimate_process_parameters) or a numeric vector of length 8 giving the process parameters.
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 ldmppr_fit.
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.
thinning: TRUE or FALSE indicating whether to use the thinned simulated values.
edge_correction: type of edge correction to apply ("none" or "toroidal").
competition_radius: distance for competition radius if include_comp_inds = TRUE.
n_sim: number of simulated datasets to generate.
save_sims: TRUE or FALSE indicating whether to save and return the simulated metrics.
verbose: TRUE or FALSE indicating whether to show progress of model checking.
seed: integer seed for reproducibility.

Value

an object of class "ldmppr_model_check".

Details

This function relies on the spatstat package for the calculation of the point pattern metrics and the GET package for the global envelope tests. The L, F, G, J, E, and V functions are a collection of non-parametric summary statistics that describe the spatial distribution of points and marks in a point pattern. See the documentation for Lest(), Fest(), Gest(), Jest(), Emark(), and Vmark() for more information. Also, see the global_envelope_test() function for more information on the global envelope tests.

References

Baddeley, A., Rubak, E., & Turner, R. (2015). *Spatial Point Patterns: Methodology and Applications with R*. Chapman and Hall/CRC Press, London. ISBN 9781482210200. Available at: https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200.

Myllymäki, M., & Mrkvička, T. (2023). GET: Global envelopes in R. arXiv:1911.06583 [stat.ME]. doi:10.48550/arXiv.1911.06583 .

Examples

# Note: The example below is provided for illustrative purposes and may take some time to run.
# \donttest{
# Load the small example data
data(small_example_data)

# Load the example mark model that previously was trained on the small example data
file_path <- system.file("extdata", "example_mark_model.rds", package = "ldmppr")
mark_model <- load_mark_model(file_path)

# 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)

# Generate the reference pattern
reference_data <- generate_mpp(
  locations = small_example_data[, c("x", "y")],
  marks = small_example_data$size,
  xy_bounds = c(0, 25, 0, 25)
)
#> Registered S3 method overwritten by 'spatstat.geom':
#>   method       from     
#>   print.metric yardstick


# Specify the estimated parameters of the self-correcting process
# Note: These would generally be estimated using estimate_process_parameters.
# These values are estimates from the small_example_data generating script.
estimated_parameters <- c(
  0.05167978, 8.20702166, 0.02199940, 2.63236890,
  1.82729512, 0.65330061, 0.86666748, 0.04681878
)

# Check the model fit
example_model_fit <- check_model_fit(
  reference_data = reference_data,
  t_min = 0,
  t_max = 1,
  process = "self_correcting",
  process_fit = estimated_parameters,
  raster_list = scaled_raster_list,
  scaled_rasters = TRUE,
  mark_model = mark_model,
  xy_bounds = c(0, 25, 0, 25),
  include_comp_inds = TRUE,
  thinning = TRUE,
  edge_correction = "none",
  competition_radius = 10,
  n_sim = 100,
  save_sims = FALSE,
  verbose = TRUE,
  seed = 90210
)

plot(example_model_fit, which = 'combined')
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the GET package.
#>   Please report the issue at <https://github.com/myllym/GET/issues>.
#> Warning: `aes_()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`
#> ℹ The deprecated feature was likely used in the GET package.
#>   Please report the issue at <https://github.com/myllym/GET/issues>.

# }