Advanced spatial Bayesian survival analysis for modeling geographic patterns in survival outcomes. Incorporates spatial correlation structures, disease mapping capabilities, and hierarchical Bayesian modeling for clinical research with geographic components.
Usage
spatialbayesiansurvival(
data,
time,
status,
statusLevel,
predictors,
spatial_coords,
region_id = NULL,
adjacency_matrix = "",
spatial_model = "car",
spatial_prior = "icar",
distance_method = "great_circle",
neighborhood_threshold = 50,
num_neighbors = 5,
baseline_hazard = "weibull",
piecewise_intervals = 5,
spline_knots = 5,
mcmc_samples = 5000,
mcmc_burnin = 2000,
mcmc_thin = 1,
mcmc_chains = 3,
covariate_priors = "normal_weak",
covariate_prior_sd = 10,
spatial_precision_prior = "gamma",
spatial_precision_shape = 1,
spatial_precision_rate = 0.5,
model_comparison = TRUE,
cross_validation = FALSE,
cv_folds = 5,
model_selection_criteria = "dic",
spatial_prediction = TRUE,
prediction_grid_size = 50,
prediction_time_points = "12,24,36,60",
confidence_level = 0.95,
show_model_summary = TRUE,
show_parameter_estimates = TRUE,
show_spatial_effects = TRUE,
show_residual_maps = TRUE,
show_survival_maps = TRUE,
show_hazard_maps = FALSE,
show_convergence_diagnostics = TRUE,
show_model_comparison = TRUE,
show_interpretation = TRUE,
standardize_predictors = TRUE,
include_intercept = TRUE,
missing_data_handling = "complete_cases",
parallel_processing = TRUE,
n_cores = 2,
clinical_context = "cancer_epidemiology",
set_seed = TRUE,
seed_value = 42
)Arguments
- data
The data as a data frame for spatial Bayesian survival analysis.
- time
.
- status
.
- statusLevel
.
- predictors
.
- spatial_coords
Longitude and Latitude coordinates (or X, Y coordinates)
- region_id
Administrative region or area identifier
- adjacency_matrix
Path to adjacency matrix file or leave empty for distance-based
- spatial_model
.
- spatial_prior
.
- distance_method
.
- neighborhood_threshold
Distance threshold for defining neighbors (km)
- num_neighbors
Number of nearest neighbors to consider
- baseline_hazard
.
- piecewise_intervals
Number of intervals for piecewise constant hazard
- spline_knots
Number of knots for B-spline baseline hazard
- mcmc_samples
.
- mcmc_burnin
.
- mcmc_thin
.
- mcmc_chains
.
- covariate_priors
.
- covariate_prior_sd
.
- spatial_precision_prior
.
- spatial_precision_shape
.
- spatial_precision_rate
.
- model_comparison
.
- cross_validation
.
- cv_folds
.
- model_selection_criteria
.
- spatial_prediction
.
- prediction_grid_size
.
- prediction_time_points
Comma-separated list of time points for prediction
- confidence_level
.
- show_model_summary
.
- show_parameter_estimates
.
- show_spatial_effects
.
- show_residual_maps
.
- show_survival_maps
.
- show_hazard_maps
.
- show_convergence_diagnostics
.
- show_model_comparison
.
- show_interpretation
.
- standardize_predictors
.
- include_intercept
.
- missing_data_handling
.
- parallel_processing
.
- n_cores
.
- clinical_context
.
- set_seed
.
- seed_value
.
Value
A results object containing:
results$modelSummary | a table | ||||
results$parameterEstimates | a table | ||||
results$spatialEffects | a table | ||||
results$modelFit | a table | ||||
results$spatialCorrelation | a table | ||||
results$modelComparison | a table | ||||
results$convergenceDiagnostics | a table | ||||
results$survivalPredictions | a table | ||||
results$spatialValidation | a table | ||||
results$clinicalInterpretation | a html | ||||
results$methodsExplanation | a html | ||||
results$spatialMaps | an image | ||||
results$survivalMaps | an image | ||||
results$hazardMaps | an image | ||||
results$residualMaps | an image | ||||
results$convergencePlots | an image | ||||
results$posteriorPlots | an image | ||||
results$spatialCorrelationPlot | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelSummary$asDF
as.data.frame(results$modelSummary)