R/bartMachine.R

In bartMachine: Bayesian Additive Regression Trees

bartMachine = function(
	X = NULL, 
	y = NULL, 
	Xy = NULL, 
    num_trees = 50, #found many times to not get better after this value... so let it be the default, it's faster too 
    num_burn_in = 250, 
    num_iterations_after_burn_in = 1000, 
    alpha = 0.95,
    beta = 2,
    k = 2,
    q = 0.9,
    nu = 3.0,
    prob_rule_class = 0.5,
    mh_prob_steps = c(2.5, 2.5, 4) / 9, #only the first two matter
    debug_log = FALSE,
    run_in_sample = TRUE,
    s_sq_y = "mse", # "mse" or "var"
	sig_sq_est = NULL,
    print_tree_illustrations = FALSE, #POWER USERS ONLY
    cov_prior_vec = NULL,
	interaction_constraints = NULL,
    use_missing_data = FALSE,
    covariates_to_permute = NULL, #PRIVATE
    num_rand_samps_in_library = 10000, #give the user the option to make a bigger library of random samples of normals and inv-gammas
    use_missing_data_dummies_as_covars = FALSE,
    replace_missing_data_with_x_j_bar = FALSE,
    impute_missingness_with_rf_impute = FALSE,
    impute_missingness_with_x_j_bar_for_lm = TRUE,
    mem_cache_for_speed = TRUE,
	flush_indices_to_save_RAM = TRUE,
	serialize = FALSE,
	seed = NULL,
    verbose = TRUE
	){
	build_bart_machine(
			X = X, 
			y = y, 
			Xy = Xy, 
			num_trees = num_trees,
			num_burn_in = num_burn_in, 
			num_iterations_after_burn_in = num_iterations_after_burn_in, 
			alpha = alpha,
			beta = beta,
			k = k,
			q = q,
			nu = nu,
			prob_rule_class = prob_rule_class,
			mh_prob_steps = mh_prob_steps,
			debug_log = debug_log,
			run_in_sample = run_in_sample,
			s_sq_y = s_sq_y,
			sig_sq_est = sig_sq_est,
			print_tree_illustrations = print_tree_illustrations, 
			cov_prior_vec = cov_prior_vec,
			interaction_constraints = interaction_constraints,
			use_missing_data = use_missing_data,
			covariates_to_permute = covariates_to_permute, 
			num_rand_samps_in_library = num_rand_samps_in_library, #give the user the option to make a bigger library of random samples of normals and inv-gammas
			use_missing_data_dummies_as_covars = use_missing_data_dummies_as_covars,
			replace_missing_data_with_x_j_bar = replace_missing_data_with_x_j_bar,
			impute_missingness_with_rf_impute = impute_missingness_with_rf_impute,
			impute_missingness_with_x_j_bar_for_lm = impute_missingness_with_x_j_bar_for_lm,
			mem_cache_for_speed = mem_cache_for_speed,
			flush_indices_to_save_RAM = flush_indices_to_save_RAM,
			serialize = serialize,
			seed = seed,
			verbose = verbose			
	)
}


bartMachineCV = function(X = NULL, y = NULL, Xy = NULL, 
   num_tree_cvs = c(50, 200),
   k_cvs = c(2, 3, 5),
   nu_q_cvs = NULL,
   k_folds = 5, 
   folds_vec = NULL,    
   verbose = FALSE, ...){
  
  	build_bart_machine_cv(X, y, Xy, 
	   num_tree_cvs,
	   k_cvs,
	   nu_q_cvs,
	   k_folds, 
	   folds_vec, ...)
}