rm bootstrapping

DESCRIPTION

@@ -73,7 +73,6 @@ Suggests:
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
 Collate: 
-    'bootstrap_class.R'
     'method_class.R'
     'analysis_class.R'
     'method_SCM_class.R'

NEWS.md

@@ -1,6 +1,7 @@
 # rdborrow 0.0.4.0
 
 ## Breaking changes
+- `setup_bootstrap()` has been removed. Bootstrap settings are now specified directly in method constructors (e.g., `ec_ipw(bootstrap = 500, bootstrap_ci_type = "perc")`). Calling `setup_bootstrap()` now raises an error with migration instructions.
 - `setup_method_weighting()`, `setup_method_DID()`, and `setup_method_SCM()` have been removed. Use `ec_ipw()`, `ec_aipw()`, `did_ec_ipw()`, `did_ec_aipw()`, `did_ec_or()`, or `scm()` instead. Calling the old constructors now raises an error with migration instructions.
 - `EC_IPW_OPT()`, `EC_AIPW_OPT()`, and all `legacy_DID_EC_*` / `legacy_SCM*` internal functions have been deleted.
 - `did_ec_ipw()` and `did_ec_aipw()`: `trt_formula` default changed from `""` to `NULL`.

R/did_ec_aipw.R

@@ -8,17 +8,13 @@ NULL
   slots = c(
     ps_formula = "character",
     trt_formula = "characterOrNULL",
-    outcome_formula = "character",
-    bootstrap = "numericOrNULL",
-    bootstrap_ci_type = "character"
+    outcome_formula = "character"
   ),
   prototype = list(
     method_name = "DID-EC-AIPW",
     ps_formula = "",
     trt_formula = NULL,
-    outcome_formula = "",
-    bootstrap = NULL,
-    bootstrap_ci_type = "perc"
+    outcome_formula = ""
   )
 )
 
@@ -84,12 +80,7 @@ did_ec_aipw <- function(ps_formula,
     outcome_formula = outcome_formula,
     bootstrap = bootstrap,
     bootstrap_ci_type = bootstrap_ci_type,
-    method_name = "DID-EC-AIPW",
-    bootstrap_flag = TRUE,
-    bootstrap_obj = .bootstrap_obj(
-      replicates = bootstrap,
-      bootstrap_CI_type = bootstrap_ci_type
-    )
+    method_name = "DID-EC-AIPW"
   )
 }
 
@@ -99,18 +90,17 @@ setMethod("estimate", "did_ec_aipw_method", function(method, data, outcomes,
                                                      covariates, alpha = 0.05,
                                                      quiet = TRUE,
                                                      T_cross) {
-  Y <- as.matrix(data[, outcomes, drop = FALSE])
-  S <- data[[trial_status]]
-  A <- data[[treatment]]
+  df <- .build_analysis_df(data, outcomes, treatment, trial_status, covariates)
+  Y <- as.matrix(df[, outcomes, drop = FALSE])
+  S <- df$S
+  A <- df$A
 
   ps_formula <- sub("^[^~]*~", paste0(trial_status, " ~"), method@ps_formula)
   trt_formula <- method@trt_formula
   if (!is.null(trt_formula)) {
     trt_formula <- sub("^[^~]*~", paste0(treatment, " ~"), trt_formula)
   }
 
-  df <- data.frame(Y, S = S, A = A, data[, covariates, drop = FALSE])
-
   if (!quiet) cat("Running DID-EC-AIPW estimator...\n")
 
   result <- .did_ec_aipw_core(
@@ -229,6 +219,8 @@ setMethod("estimate", "did_ec_aipw_method", function(method, data, outcomes,
                                         trt_formula, outcome_formula, T_cross) {
   d <- data[indices, , drop = FALSE]
   Y <- as.matrix(d[, outcomes, drop = FALSE])
-  .did_ec_aipw_core(d, Y, d$S, d$A, T_cross, ps_formula, trt_formula,
-    outcome_formula)$tau
+  .did_ec_aipw_core(
+    d, Y, d$S, d$A, T_cross, ps_formula, trt_formula,
+    outcome_formula
+  )$tau
 }

R/did_ec_ipw.R

@@ -7,16 +7,12 @@ NULL
   contains = "method_DID_obj",
   slots = c(
     ps_formula = "character",
-    trt_formula = "characterOrNULL",
-    bootstrap = "numericOrNULL",
-    bootstrap_ci_type = "character"
+    trt_formula = "characterOrNULL"
   ),
   prototype = list(
     method_name = "DID-EC-IPW",
     ps_formula = "",
-    trt_formula = NULL,
-    bootstrap = NULL,
-    bootstrap_ci_type = "perc"
+    trt_formula = NULL
   )
 )
 
@@ -71,12 +67,7 @@ did_ec_ipw <- function(ps_formula,
     trt_formula = trt_formula,
     bootstrap = bootstrap,
     bootstrap_ci_type = bootstrap_ci_type,
-    method_name = "DID-EC-IPW",
-    bootstrap_flag = TRUE,
-    bootstrap_obj = .bootstrap_obj(
-      replicates = bootstrap,
-      bootstrap_CI_type = bootstrap_ci_type
-    )
+    method_name = "DID-EC-IPW"
   )
 }
 
@@ -86,18 +77,17 @@ setMethod("estimate", "did_ec_ipw_method", function(method, data, outcomes,
                                                     covariates, alpha = 0.05,
                                                     quiet = TRUE,
                                                     T_cross) {
-  Y <- as.matrix(data[, outcomes, drop = FALSE])
-  S <- data[[trial_status]]
-  A <- data[[treatment]]
+  df <- .build_analysis_df(data, outcomes, treatment, trial_status, covariates)
+  Y <- as.matrix(df[, outcomes, drop = FALSE])
+  S <- df$S
+  A <- df$A
 
   ps_formula <- sub("^[^~]*~", paste0(trial_status, " ~"), method@ps_formula)
   trt_formula <- method@trt_formula
   if (!is.null(trt_formula)) {
     trt_formula <- sub("^[^~]*~", paste0(treatment, " ~"), trt_formula)
   }
 
-  df <- data.frame(Y, S = S, A = A, data[, covariates, drop = FALSE])
-
   if (!quiet) cat("Running DID-EC-IPW estimator...\n")
 
   result <- .did_ec_ipw_core(df, Y, S, A, T_cross, ps_formula, trt_formula)

R/did_ec_or.R

@@ -8,17 +8,13 @@ NULL
   slots = c(
     outcome_formula_ext = "character",
     outcome_formula_rct_ctrl = "character",
-    outcome_formula_rct_trt = "character",
-    bootstrap = "numericOrNULL",
-    bootstrap_ci_type = "character"
+    outcome_formula_rct_trt = "character"
   ),
   prototype = list(
     method_name = "DID-EC-OR",
     outcome_formula_ext = "",
     outcome_formula_rct_ctrl = "",
-    outcome_formula_rct_trt = "",
-    bootstrap = NULL,
-    bootstrap_ci_type = "perc"
+    outcome_formula_rct_trt = ""
   )
 )
 
@@ -86,12 +82,7 @@ did_ec_or <- function(outcome_formula_ext,
     outcome_formula_rct_trt = outcome_formula_rct_trt,
     bootstrap = bootstrap,
     bootstrap_ci_type = bootstrap_ci_type,
-    method_name = "DID-EC-OR",
-    bootstrap_flag = TRUE,
-    bootstrap_obj = .bootstrap_obj(
-      replicates = bootstrap,
-      bootstrap_CI_type = bootstrap_ci_type
-    )
+    method_name = "DID-EC-OR"
   )
 }
 
@@ -103,11 +94,9 @@ setMethod("estimate", "did_ec_or_method", function(method, data, outcomes,
                                                    covariates, alpha = 0.05,
                                                    quiet = TRUE,
                                                    T_cross) {
-  Y <- as.matrix(data[, outcomes, drop = FALSE])
-  S <- data[[trial_status]]
-  A <- data[[treatment]]
-
-  df <- data.frame(Y, S = S, A = A, data[, covariates, drop = FALSE])
+  df <- .build_analysis_df(data, outcomes, treatment, trial_status, covariates)
+  S <- df$S
+  A <- df$A
 
   if (!quiet) cat("Running DID-EC-OR estimator...\n")
 
@@ -121,7 +110,7 @@ setMethod("estimate", "did_ec_or_method", function(method, data, outcomes,
 
   if (!quiet) cat("Running bootstrap inference...\n")
 
-  n_ole <- ncol(Y) - T_cross
+  n_ole <- length(outcomes) - T_cross
   boot_res <- .run_bootstrap(
     df = df, statistic = .did_ec_or_boot_statistic,
     n_estimates = n_ole, bootstrap = method@bootstrap,
@@ -137,7 +126,7 @@ setMethod("estimate", "did_ec_or_method", function(method, data, outcomes,
     point_estimates = tau,
     lower_CI_boot = boot_res$lower_ci,
     upper_CI_boot = boot_res$upper_ci,
-    row.names = paste0("tau", (T_cross + 1):ncol(Y))
+    row.names = paste0("tau", (T_cross + 1):length(outcomes))
   )
 })
 
@@ -216,6 +205,8 @@ setMethod("estimate", "did_ec_or_method", function(method, data, outcomes,
                                       outcome_formula_rct_trt,
                                       T_cross) {
   d <- data[indices, , drop = FALSE]
-  .did_ec_or_core(d, d$S, d$A, T_cross, outcome_formula_ext,
-    outcome_formula_rct_ctrl, outcome_formula_rct_trt)$tau
+  .did_ec_or_core(
+    d, d$S, d$A, T_cross, outcome_formula_ext,
+    outcome_formula_rct_ctrl, outcome_formula_rct_trt
+  )$tau
 }

R/ec_aipw.R

@@ -8,17 +8,13 @@ NULL
   slots = c(
     ps_formula = "character",
     outcome_formula = "character",
-    weight = "numericOrNULL",
-    bootstrap = "numericOrNULL",
-    bootstrap_ci_type = "characterOrNULL"
+    weight = "numericOrNULL"
   ),
   prototype = list(
     method_name = "EC-AIPW",
     ps_formula = "",
     outcome_formula = "",
-    weight = NULL,
-    bootstrap = NULL,
-    bootstrap_ci_type = NULL
+    weight = NULL
   )
 )
 
@@ -104,12 +100,7 @@ ec_aipw <- function(ps_formula,
     weight = weight,
     bootstrap = bootstrap,
     bootstrap_ci_type = bootstrap_ci_type,
-    method_name = "EC-AIPW",
-    bootstrap_flag = !is.null(bootstrap),
-    bootstrap_obj = .bootstrap_obj(
-      replicates = bootstrap %||% 500L,
-      bootstrap_CI_type = bootstrap_ci_type %||% "perc"
-    )
+    method_name = "EC-AIPW"
   )
 }
 
@@ -126,7 +117,7 @@ setMethod("estimate", "ec_aipw_method", function(method, data, outcomes,
       call. = FALSE
     )
   }
-  df <- build_analysis_df(method, data, outcomes, treatment, trial_status, covariates)
+  df <- .build_analysis_df(data, outcomes, treatment, trial_status, covariates)
   n_time <- length(outcomes)
 
   if (!quiet) cat("Running EC-AIPW estimator...\n")
@@ -185,7 +176,6 @@ setMethod("estimate", "ec_aipw_method", function(method, data, outcomes,
 #' @return list with tau, borrow_weight, and model intermediates.
 #' @noRd
 .ec_aipw_core <- function(df, outcomes, ps_formula, outcome_formula, weight) {
-
   # see Zhou 2024a: Def 2 (Eq 7) for point estimate, Eq 11 for optimal weight
 
   Y <- as.matrix(df[, outcomes, drop = FALSE])
@@ -255,7 +245,6 @@ setMethod("estimate", "ec_aipw_method", function(method, data, outcomes,
 #' @return numeric vector of standard errors (length n_time).
 #' @noRd
 .ec_aipw_se <- function(df, core, n_time, outcome_formula) {
-
   # see Zhou 2024a: Theorem 4 (Eq 15 for A/B matrices, Eq 16 for variance)
 
   S <- df$S
@@ -354,7 +343,7 @@ setMethod("estimate", "ec_aipw_method", function(method, data, outcomes,
 #' @return numeric vector of tau estimates.
 #' @noRd
 .ec_aipw_boot_statistic <- function(data, indices, outcomes, covariates,
-                               ps_formula, outcome_formula, borrow_wt) {
+                                    ps_formula, outcome_formula, borrow_wt) {
   d <- data[indices, , drop = FALSE]
   core <- .ec_aipw_core(d, outcomes, ps_formula, outcome_formula, borrow_wt)
   core$tau