update with names

ncn-foreigners · Feb 4, 2025 · b6c5a58 · b6c5a58
1 parent 3084bc1
commit b6c5a58
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Showing 2 changed files with 26 additions and 26 deletions.
diff --git a/R/nonprob_dr.R b/R/nonprob_dr.R
@@ -254,20 +254,20 @@ nonprob_dr <- function(selection,
         combined_terms <- union(terms_out, terms_sel)
         combined_formula <- as.formula(paste(outcome[2], paste(combined_terms, collapse = " + "), sep = " ~ "))
 
-        Model <- model_frame(
+        outcome_model_data <- model_frame(
           formula = combined_formula,
           data = data,
           svydesign = svydesign
         )
-        outcome_model_data <- selection_model_data <- Model
-        n_nons <- nrow(Model$X_nons)
-        n_rand <- nrow(Model$X_rand)
-        R_nons <- rep(1, nrow(Model$X_nons))
-        R_rand <- rep(0, nrow(Model$X_rand))
+        outcome_model_data <- selection_model_data <- outcome_model_data
+        n_nons <- nrow(outcome_model_data$X_nons)
+        n_rand <- nrow(outcome_model_data$X_rand)
+        R_nons <- rep(1, nrow(outcome_model_data$X_nons))
+        R_rand <- rep(0, nrow(outcome_model_data$X_rand))
         R <- c(R_rand, R_nons)
         y_rand <- vector(mode = "numeric", length = n_rand)
-        y <- c(y_rand, Model$y_nons) # outcome variable for joint model
-        X <- rbind(Model$X_rand, Model$X_nons)
+        y <- c(y_rand, outcome_model_data$y_nons) # outcome variable for joint model
+        X <- rbind(outcome_model_data$X_rand, outcome_model_data$X_nons)
 
         ############ working version
         if (var_selection == TRUE) {
@@ -277,7 +277,7 @@ nonprob_dr <- function(selection,
           }
           beta <- ncvreg::cv.ncvreg(
             X = X[loc_nons, -1],
-            y = Model$y_nons,
+            y = outcome_model_data$y_nons,
             penalty = control_outcome$penalty,
             family = family_outcome,
             nlambda = nlambda,
@@ -301,8 +301,8 @@ nonprob_dr <- function(selection,
           Xsel <- as.matrix(X[, idx + 1, drop = FALSE])
           X <- cbind(1, Xsel)
           colnames(X) <- c("(Intercept)", colnames(Xsel))
-          Model$X_rand <- X[loc_rand, ]
-          Model$X_nons <- X[loc_nons, ]
+          outcome_model_data$X_rand <- X[loc_rand, ]
+          outcome_model_data$X_nons <- X[loc_nons, ]
         }
         estimation_model <- mm(
           X = X,
@@ -347,11 +347,11 @@ nonprob_dr <- function(selection,
         weights_nons <- 1 / ps_nons
         N_nons <- sum(weights * weights_nons)
         N_rand <- sum(weights_rand)
-        y_nons <- Model$y_nons
+        y_nons <- outcome_model_data$y_nons
 
         if (is.null(pop_size)) pop_size <- N_nons
         mu_hat <- mu_hatDR(
-          y = Model$y_nons,
+          y = outcome_model_data$y_nons,
           y_nons = y_nons_pred,
           y_rand = y_rand_pred,
           weights = weights,

diff --git a/R/nonprob_mi.R b/R/nonprob_mi.R
@@ -71,11 +71,11 @@ nonprob_mi <- function(outcome,
       cond <- TRUE
       kk <- control_outcome$k - 1
       while (cond) {
-        OutcomeModel <- model_frame(formula = outcome, data = data, svydesign = svydesign)
-        X_nons <- OutcomeModel$X_nons
-        X_rand <- OutcomeModel$X_rand
-        nons_names <- OutcomeModel$nons_names
-        y_nons <- OutcomeModel$y_nons
+        outcome_model_data <- model_frame(formula = outcome, data = data, svydesign = svydesign)
+        X_nons <- outcome_model_data$X_nons
+        X_rand <- outcome_model_data$X_rand
+        nons_names <- outcome_model_data$nons_names
+        y_nons <- outcome_model_data$y_nons
 
         R_nons <- rep(1, nrow(X_nons))
         R_rand <- rep(0, nrow(X_rand))
@@ -109,7 +109,7 @@ nonprob_mi <- function(outcome,
           control = control_outcome,
           n_nons = n_nons,
           n_rand = n_rand,
-          model_frame = OutcomeModel$model_frame_rand,
+          model_frame = outcome_model_data$model_frame_rand,
           vars_selection = control_inference$vars_selection,
           pop_totals = pop_totals
         )
@@ -165,11 +165,11 @@ nonprob_mi <- function(outcome,
       outcome <- outcomes$outcome[[k]]
 
       # model for outcome formula
-      OutcomeModel <- model_frame(formula = outcome, data = data, svydesign = svydesign)
-      X_nons <- OutcomeModel$X_nons
-      X_rand <- OutcomeModel$X_rand
-      nons_names <- OutcomeModel$nons_names
-      y_nons <- OutcomeModel$y_nons
+      outcome_model_data <- model_frame(formula = outcome, data = data, svydesign = svydesign)
+      X_nons <- outcome_model_data$X_nons
+      X_rand <- outcome_model_data$X_rand
+      nons_names <- outcome_model_data$nons_names
+      y_nons <- outcome_model_data$y_nons
 
       R_nons <- rep(1, nrow(X_nons))
       R_rand <- rep(0, nrow(X_rand))
@@ -237,15 +237,15 @@ nonprob_mi <- function(outcome,
         control = control_outcome,
         n_nons = n_nons,
         n_rand = n_rand,
-        model_frame = OutcomeModel$model_frame_rand,
+        model_frame = outcome_model_data$model_frame_rand,
         vars_selection = control_inference$vars_selection,
         pop_totals = pop_totals
       )
       y_rand_pred <- model_obj$y_rand_pred
       y_nons_pred <- model_obj$y_nons_pred
       # parameters <- model_obj$parameters
       outcome_list[[k]] <- model_obj$model
-      outcome_list[[k]]$model_frame <- OutcomeModel$model_frame_rand
+      outcome_list[[k]]$model_frame <- outcome_model_data$model_frame_rand
 
       # updating probability sample by adding y_hat variable
       svydesign <- stats::update(svydesign,