Found and fixed other uses of shorthand T/F instead of TRUE/FALSE

Author: andrewherren

R/bart.R

@@ -555,7 +555,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
     outcome_train <- createOutcome(resid_train)
 
     # Random number generator (std::mt19937)
-    if (is.null(random_seed)) random_seed = sample(1:10000,1,F)
+    if (is.null(random_seed)) random_seed = sample(1:10000,1,FALSE)
     rng <- createCppRNG(random_seed)
 
     # Sampling data structures
@@ -632,7 +632,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
         if (requires_basis) init_values_mean_forest <- rep(0., ncol(leaf_basis_train))
         else init_values_mean_forest <- 0.
         active_forest_mean$prepare_for_sampler(forest_dataset_train, outcome_train, forest_model_mean, leaf_model_mean_forest, init_values_mean_forest)
-        active_forest_mean$adjust_residual(forest_dataset_train, outcome_train, forest_model_mean, requires_basis, F)
+        active_forest_mean$adjust_residual(forest_dataset_train, outcome_train, forest_model_mean, requires_basis, FALSE)
     }
 
     # Initialize the leaves of each tree in the variance forest
@@ -645,7 +645,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
     if (num_gfr > 0){
         for (i in 1:num_gfr) {
             # Keep all GFR samples at this stage -- remove from ForestSamples after MCMC
-            # keep_sample <- ifelse(keep_gfr, T, F)
+            # keep_sample <- ifelse(keep_gfr, TRUE, FALSE)
             keep_sample <- TRUE
             if (keep_sample) sample_counter <- sample_counter + 1
             # Print progress

R/bcf.R

@@ -813,7 +813,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id
     outcome_train <- createOutcome(resid_train)
 
     # Random number generator (std::mt19937)
-    if (is.null(random_seed)) random_seed = sample(1:10000,1,F)
+    if (is.null(random_seed)) random_seed = sample(1:10000,1,FALSE)
     rng <- createCppRNG(random_seed)
 
     # Sampling data structures
@@ -840,10 +840,10 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id
     }
 
     # Container of forest samples
-    forest_samples_mu <- createForestSamples(num_trees_mu, 1, T)
-    forest_samples_tau <- createForestSamples(num_trees_tau, 1, F)
-    active_forest_mu <- createForest(num_trees_mu, 1, T)
-    active_forest_tau <- createForest(num_trees_tau, 1, F)
+    forest_samples_mu <- createForestSamples(num_trees_mu, 1, TRUE)
+    forest_samples_tau <- createForestSamples(num_trees_tau, 1, FALSE)
+    active_forest_mu <- createForest(num_trees_mu, 1, TRUE)
+    active_forest_tau <- createForest(num_trees_tau, 1, FALSE)
     if (include_variance_forest) {
         forest_samples_variance <- createForestSamples(num_trees_variance, 1, TRUE, TRUE)
         active_forest_variance <- createForest(num_trees_variance, 1, TRUE, TRUE)
@@ -852,12 +852,12 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id
     # Initialize the leaves of each tree in the prognostic forest
     init_mu <- mean(resid_train)
     active_forest_mu$prepare_for_sampler(forest_dataset_train, outcome_train, forest_model_mu, 0, init_mu)
-    active_forest_mu$adjust_residual(forest_dataset_train, outcome_train, forest_model_mu, F, F)
+    active_forest_mu$adjust_residual(forest_dataset_train, outcome_train, forest_model_mu, FALSE, FALSE)
 
     # Initialize the leaves of each tree in the treatment effect forest
     init_tau <- 0.
     active_forest_tau$prepare_for_sampler(forest_dataset_train, outcome_train, forest_model_tau, 1, init_tau)
-    active_forest_tau$adjust_residual(forest_dataset_train, outcome_train, forest_model_tau, T, F)
+    active_forest_tau$adjust_residual(forest_dataset_train, outcome_train, forest_model_tau, TRUE, FALSE)
 
     # Initialize the leaves of each tree in the variance forest
     if (include_variance_forest) {
@@ -868,7 +868,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id
     if (num_gfr > 0){
         for (i in 1:num_gfr) {
             # Keep all GFR samples at this stage -- remove from ForestSamples after MCMC
-            # keep_sample <- ifelse(keep_gfr, T, F)
+            # keep_sample <- ifelse(keep_gfr, TRUE, FALSE)
             keep_sample <- TRUE
             if (keep_sample) sample_counter <- sample_counter + 1
             # Print progress

R/forest.R

@@ -231,14 +231,14 @@ ForestSamples <- R6::R6Class(
 
         #' @description
         #' Return constant leaf status of trees in a `ForestContainer` object
-        #' @return `T` if leaves are constant, `F` otherwise
+        #' @return `TRUE` if leaves are constant, `FALSE` otherwise
         is_constant_leaf = function() {
             return(is_constant_leaf_forest_container_cpp(self$forest_container_ptr))
         }, 
 
         #' @description
         #' Return exponentiation status of trees in a `ForestContainer` object
-        #' @return `T` if leaf predictions must be exponentiated, `F` otherwise
+        #' @return `TRUE` if leaf predictions must be exponentiated, `FALSE` otherwise
         is_exponentiated = function() {
             return(is_exponentiated_forest_container_cpp(self$forest_container_ptr))
         }, 
@@ -692,14 +692,14 @@ Forest <- R6::R6Class(
 
         #' @description
         #' Return constant leaf status of trees in a `Forest` object
-        #' @return `T` if leaves are constant, `F` otherwise
+        #' @return `TRUE` if leaves are constant, `FALSE` otherwise
         is_constant_leaf = function() {
             return(is_constant_leaf_active_forest_cpp(self$forest_ptr))
         }, 
 
         #' @description
         #' Return exponentiation status of trees in a `Forest` object
-        #' @return `T` if leaf predictions must be exponentiated, `F` otherwise
+        #' @return `TRUE` if leaf predictions must be exponentiated, `FALSE` otherwise
         is_exponentiated = function() {
             return(is_exponentiated_active_forest_cpp(self$forest_ptr))
         }, 

R/kernel.R

@@ -150,7 +150,7 @@ computeForestLeafVariances <- function(model_object, forest_type, forest_inds=NU
             if (!model_object$model_params$include_mean_forest) {
                 stop("Mean forest was not sampled in the bart model provided")
             }
-            if (model_object$model_params$sample_sigma_leaf == F) {
+            if (!model_object$model_params$sample_sigma_leaf) {
                 stop("Leaf scale parameter was not sampled for the mean forest in the bart model provided")
             }
             leaf_scale_vector <- model_object$sigma2_leaf_samples
@@ -163,12 +163,12 @@ computeForestLeafVariances <- function(model_object, forest_type, forest_inds=NU
     } else {
         stopifnot(forest_type %in% c("prognostic", "treatment", "variance"))
         if (forest_type=="prognostic") {
-            if (model_object$model_params$sample_sigma_leaf_mu == F) {
+            if (!model_object$model_params$sample_sigma_leaf_mu) {
                 stop("Leaf scale parameter was not sampled for the prognostic forest in the bcf model provided")
             }
             leaf_scale_vector <- model_object$sigma_leaf_mu_samples
         } else if (forest_type=="treatment") {
-            if (model_object$model_params$sample_sigma_leaf_tau == F) {
+            if (!model_object$model_params$sample_sigma_leaf_tau) {
                 stop("Leaf scale parameter was not sampled for the treatment effect forest in the bcf model provided")
             }
             leaf_scale_vector <- model_object$sigma_leaf_tau_samples

R/utils.R

@@ -177,7 +177,7 @@ preprocessTrainDataFrame <- function(input_df) {
     # Detect ordered and unordered categorical variables
 
     # First, ordered categorical: users must have explicitly 
-    # converted this to a factor with ordered = T
+    # converted this to a factor with ordered = TRUE
     factor_mask <- sapply(input_df, is.factor)
     ordered_mask <- sapply(input_df, is.ordered)
     ordered_cat_matches <- factor_mask & ordered_mask
@@ -528,9 +528,9 @@ createForestCovariates <- function(input_data, ordered_cat_vars = NULL, unordere
         stop("input_data must be either a matrix or a data frame")
     }
     df_vars <- names(input_df)
-    if (is.null(ordered_cat_vars)) ordered_cat_matches <- rep(F, length(df_vars))
+    if (is.null(ordered_cat_vars)) ordered_cat_matches <- rep(FALSE, length(df_vars))
     else ordered_cat_matches <- df_vars %in% ordered_cat_vars
-    if (is.null(unordered_cat_vars)) unordered_cat_matches <- rep(F, length(df_vars))
+    if (is.null(unordered_cat_vars)) unordered_cat_matches <- rep(FALSE, length(df_vars))
     else unordered_cat_matches <- df_vars %in% unordered_cat_vars
     numeric_matches <- ((!ordered_cat_matches) & (!unordered_cat_matches))
     ordered_cat_vars <- df_vars[ordered_cat_matches]

man/Forest.Rd

@@ -244,7 +244,7 @@ test_that("Warmstart BART", {
         ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5) + 
         ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5)
     )
-    rfx_group_ids <- sample(1:2, size = n, replace = T)
+    rfx_group_ids <- sample(1:2, size = n, replace = TRUE)
     rfx_basis <- rep(1, n)
     rfx_coefs <- c(-5, 5)
     rfx_term <- rfx_coefs[rfx_group_ids] * rfx_basis

man/ForestSamples.Rd

@@ -298,7 +298,7 @@ test_that("Warmstart BCF", {
             ((0.75 <= X[,2]) & (1 > X[,2])) * (2.0)
     )
     Z <- rbinom(n, 1, pi_X)
-    rfx_group_ids <- sample(1:2, size = n, replace = T)
+    rfx_group_ids <- sample(1:2, size = n, replace = TRUE)
     rfx_basis <- rep(1, n)
     rfx_coefs <- c(-5, 5)
     rfx_term <- rfx_coefs[rfx_group_ids] * rfx_basis

test/R/testthat/test-bart.R

@@ -12,7 +12,7 @@ test_that("In-sample one-hot encoding works for unordered categorical variables"
           0,1,0,0,0,
           0,0,0,1,0,
           0,1,0,0,0),
-    byrow = T, ncol = 5)
+    byrow = TRUE, ncol = 5)
     x1_levels_expected <- c("1","2","3","4")
 
     x2 <- c("a","c","b","c","d","a","c","a","b","d")
@@ -28,7 +28,7 @@ test_that("In-sample one-hot encoding works for unordered categorical variables"
           1,0,0,0,0,
           0,1,0,0,0,
           0,0,0,1,0),
-        byrow = T, ncol = 5)
+        byrow = TRUE, ncol = 5)
     x2_levels_expected <- c("a","b","c","d")
 
     x3 <- c(3.2,2.4,1.5,4.6,3.2,2.4,3.2,2.4,4.6,2.4)
@@ -44,7 +44,7 @@ test_that("In-sample one-hot encoding works for unordered categorical variables"
           0,1,0,0,0,
           0,0,0,1,0,
           0,1,0,0,0),
-        byrow = T, ncol = 5)
+        byrow = TRUE, ncol = 5)
     x3_levels_expected <- c("1.5","2.4","3.2","4.6")
 
     expect_equal(x1_onehot$Xtilde, x1_expected)
@@ -65,7 +65,7 @@ test_that("Out-of-sample one-hot encoding works for unordered categorical variab
           0,0,0,1,0,
           0,0,1,0,0,
           0,0,0,0,1),
-        byrow = T, ncol = 5)
+        byrow = TRUE, ncol = 5)
 
     x2 <- c("a","c","b","c","d","a","c","a","b","d")
     x2_test <- c("a","c","g","b","f")
@@ -76,7 +76,7 @@ test_that("Out-of-sample one-hot encoding works for unordered categorical variab
           0,0,0,0,1,
           0,1,0,0,0,
           0,0,0,0,1),
-        byrow = T, ncol = 5)
+        byrow = TRUE, ncol = 5)
 
     x3 <- c(3.2,2.4,1.5,4.6,3.2,2.4,3.2,2.4,4.6,2.4)
     x3_test <- c(10.3,-0.5,4.6,3.2,1.8)
@@ -87,7 +87,7 @@ test_that("Out-of-sample one-hot encoding works for unordered categorical variab
           0,0,0,1,0,
           0,0,1,0,0,
           0,0,0,0,1),
-        byrow = T, ncol = 5)
+        byrow = TRUE, ncol = 5)
 
     expect_equal(x1_test_onehot, x1_test_expected)
     expect_equal(x2_test_onehot, x2_test_expected)
@@ -102,13 +102,13 @@ test_that("In-sample preprocessing for ordered categorical variables", {
     x1_vector_expected <- c(1,3,2,4,3,5,4)
     x1_levels_expected <- string_var_response_levels
 
-    x2 <- factor(x1, levels = string_var_response_levels, ordered = T)
+    x2 <- factor(x1, levels = string_var_response_levels, ordered = TRUE)
     x2_preprocessing <- orderedCatInitializeAndPreprocess(x2)
     x2_vector_expected <- c(1,3,2,4,3,5,4)
     x2_levels_expected <- string_var_response_levels
 
     string_var_levels_reordered <- c("5. Strongly agree", "4. Agree", "3. Neither agree nor disagree", "2. Disagree", "1. Strongly disagree")
-    x3 <- factor(x1, levels = string_var_levels_reordered, ordered = T)
+    x3 <- factor(x1, levels = string_var_levels_reordered, ordered = TRUE)
     x3_preprocessing <- orderedCatInitializeAndPreprocess(x3)
     x3_vector_expected <- c(5,3,4,2,3,1,2)
     x3_levels_expected <- string_var_levels_reordered
@@ -135,7 +135,7 @@ test_that("Out-of-sample preprocessing for ordered categorical variables", {
     x1_preprocessing <- orderedCatPreprocess(x1, string_var_response_levels)
     x1_vector_expected <- c(1,3,2,4,3,5,4)
 
-    x2 <- factor(x1, levels = string_var_response_levels, ordered = T)
+    x2 <- factor(x1, levels = string_var_response_levels, ordered = TRUE)
     x2_preprocessing <- orderedCatPreprocess(x2, string_var_response_levels)
     x2_vector_expected <- c(1,3,2,4,3,5,4)
 

test/R/testthat/test-bcf.R

@@ -7,11 +7,11 @@ test_that("Prediction from trees with constant leaf", {
                  4.4, 5.4, 10.4, 
                  5.3, 9.3, 3.6, 
                  6.1, 10.4, 4.4), 
-               byrow = T, nrow = 6)
+               byrow = TRUE, nrow = 6)
     n <- nrow(X)
     p <- ncol(X)
     forest_dataset = createForestDataset(X)
-    forest_samples <- createForestSamples(num_trees, 1, T)
+    forest_samples <- createForestSamples(num_trees, 1, TRUE)
 
     # Initialize a forest with constant root predictions
     forest_samples$add_forest_with_constant_leaves(0.)
@@ -60,12 +60,12 @@ test_that("Prediction from trees with univariate leaf basis", {
                  4.4, 5.4, 10.4, 
                  5.3, 9.3, 3.6, 
                  6.1, 10.4, 4.4), 
-               byrow = T, nrow = 6)
+               byrow = TRUE, nrow = 6)
     W = as.matrix(c(-1,-1,-1,1,1,1))
     n <- nrow(X)
     p <- ncol(X)
     forest_dataset = createForestDataset(X,W)
-    forest_samples <- createForestSamples(num_trees, 1, F)
+    forest_samples <- createForestSamples(num_trees, 1, FALSE)
 
     # Initialize a forest with constant root predictions
     forest_samples$add_forest_with_constant_leaves(0.)
@@ -118,12 +118,12 @@ test_that("Prediction from trees with multivariate leaf basis", {
                  4.4, 5.4, 10.4, 
                  5.3, 9.3, 3.6, 
                  6.1, 10.4, 4.4), 
-               byrow = T, nrow = 6)
+               byrow = TRUE, nrow = 6)
     n <- nrow(X)
     p <- ncol(X)
     W = matrix(c(1,1,1,1,1,1,-1,-1,-1,1,1,1), byrow=FALSE, nrow=6)
     forest_dataset = createForestDataset(X,W)
-    forest_samples <- createForestSamples(num_trees, output_dim, F)
+    forest_samples <- createForestSamples(num_trees, output_dim, FALSE)
 
     # Initialize a forest with constant root predictions
     forest_samples$add_forest_with_constant_leaves(c(1.,1.))

test/R/testthat/test-categorical.R

@@ -7,7 +7,7 @@ test_that("Residual updates correctly propagated after forest sampling step", {
                  4.4, 5.4, 10.4, 
                  5.3, 9.3, 3.6, 
                  6.1, 10.4, 4.4), 
-               byrow = T, nrow = 6)
+               byrow = TRUE, nrow = 6)
     W = matrix(c(1, 1, 1, 1, 1, 1), nrow = 6)
     n = nrow(X)
     p = ncol(X)
@@ -44,17 +44,17 @@ test_that("Residual updates correctly propagated after forest sampling step", {
                                                   variable_weights=variable_weights, variance_forest_shape=a_forest, 
                                                   variance_forest_scale=b_forest, cutpoint_grid_size=cutpoint_grid_size)
     forest_model = createForestModel(forest_dataset, forest_model_config, global_model_config)
-    forest_samples = createForestSamples(num_trees, 1, F)
-    active_forest = createForest(num_trees, 1, F)
+    forest_samples = createForestSamples(num_trees, 1, FALSE)
+    active_forest = createForest(num_trees, 1, FALSE)
 
     # Initialize the leaves of each tree in the prognostic forest
     active_forest$prepare_for_sampler(forest_dataset, residual, forest_model, 0, mean(resid))
-    active_forest$adjust_residual(forest_dataset, residual, forest_model, F, F)
+    active_forest$adjust_residual(forest_dataset, residual, forest_model, FALSE, FALSE)
 
     # Run the forest sampling algorithm for a single iteration
     forest_model$sample_one_iteration(
         forest_dataset, residual, forest_samples, active_forest, 
-        cpp_rng, forest_model_config, global_model_config, keep_forest = T, gfr = T
+        cpp_rng, forest_model_config, global_model_config, keep_forest = TRUE, gfr = TRUE
     )
 
     # Get the current residual after running the sampler