run_abrm: Run ABRM Analysis

Description

Runs the Atom-Based Regression Model on simulated data

Usage

run_abrm(
  gridx,
  gridy,
  atoms,
  model_code,
  true_params = NULL,
  norm_idx_x = NULL,
  pois_idx_x = NULL,
  binom_idx_x = NULL,
  norm_idx_y = NULL,
  pois_idx_y = NULL,
  binom_idx_y = NULL,
  dist_y = 2,
  niter = 50000,
  nburnin = 30000,
  nchains = 2,
  thin = 10,
  sim_metadata = NULL,
  save_plots = TRUE,
  output_dir = NULL
)

Value

List containing MCMC results and parameter estimates

Arguments

gridx: The X-grid sf dataframe, containing a numeric area ID variable named 'ID' and covariates named 'covariate_x_1','covariate_x_2',...
gridy: The Y-grid sf dataframe, containing a numeric area ID variable named 'ID', covariates named 'covariate_y_1','covariate_y_2',...., and an outcome named 'y'.
atoms: The atom sf dataframe, which should contain numeric variables named 'ID_x' and 'ID_y' holding the X-grid and Y-grid cell IDs for each atom, as well as an atom-level population count named 'population'.
model_code: NIMBLE model code from get_abrm_model()
true_params: The true outcome model regression coefficient parameters, if known (e.g., from simulate_misaligned_data())
norm_idx_x: Vector of numeric indices of X-grid covariates (ordered as 'covariate_x_1','covariate_x_2',...) that should be treated as normally-distributed
pois_idx_x: Vector of numeric indices of X-grid covariates (ordered as 'covariate_x_1','covariate_x_2',...) that should be treated as Poisson-distributed
binom_idx_x: Vector of numeric indices of X-grid covariates (ordered as 'covariate_x_1','covariate_x_2',...) that should be treated as binomial-distributed
norm_idx_y: Vector of numeric indices of Y-grid covariates (ordered as 'covariate_y_1','covariate_y_2',...) that should be treated as normally-distributed
pois_idx_y: Vector of numeric indices of Y-grid covariates (ordered as 'covariate_y_1','covariate_y_2',...) that should be treated as Poisson-distributed
binom_idx_y: Vector of numeric indices of Y-grid covariates (ordered as 'covariate_y_1','covariate_y_2',...) that should be treated as binomial-distributed
dist_y: Distribution type for outcome (1=normal, 2=poisson, 3=binomial)
niter: Number of MCMC iterations (default: 50000)
nburnin: Number of burn-in iterations (default: 30000)
nchains: Number of MCMC chains (default: 2)
thin: Thinning interval (default: 10)
sim_metadata: Optional simulation metadata list
save_plots: Logical, whether to save diagnostic plots (default: TRUE)
output_dir: Directory for saving outputs (default: NULL)