shorten variable names

Author: danielinteractive

R/survival.R

@@ -86,7 +86,7 @@ h_lr_test_via_score <- function(score_fun, ...) {
     tau_l = tau_l,
     pval = pval,
     n = n,
-    give_randomization_strata_warning = score_attrs$give_randomization_strata_warning
+    give_rand_strat_warning = score_attrs$give_rand_strat_warning
   )
 }
 
@@ -162,7 +162,7 @@ robin_surv_comparison <- function(
     if (!is.null(unadj_score_fun)) {
       assert_function(unadj_score_fun)
       assert_true(length(vars$covariates) > 0)
-      args_to_drop <- c("model", "hr_se_plugin_adjusted", "check_randomization_strata_warning")
+      args_to_drop <- c("model", "hr_se_plugin_adjusted", "check_rand_strat_warning")
       unadj_args <- args[!(names(args) %in% args_to_drop)]
       unadj_args$score_fun <- unadj_score_fun
       # Get theta_hat from the unadjusted score function.
@@ -194,7 +194,7 @@ robin_surv_comparison <- function(
     test_score = test_result$u_l,
     test_n = test_result$n,
     test_sigma_l2 = test_result$sigma_l2,
-    give_randomization_strata_warning = test_result$give_randomization_strata_warning
+    give_rand_strat_warning = test_result$give_rand_strat_warning
   )
 }
 
@@ -218,7 +218,7 @@ robin_surv_no_strata_no_cov <- function(
   exp_level,
   control_level,
   contrast,
-  check_randomization_strata_warning = FALSE
+  check_rand_strat_warning = FALSE
 ) {
   robin_surv_comparison(
     score_fun = h_lr_score_no_strata_no_cov,
@@ -231,7 +231,7 @@ robin_surv_no_strata_no_cov <- function(
     time = vars$time,
     status = vars$status,
     randomization_strata = vars$randomization_strata,
-    check_randomization_strata_warning = check_randomization_strata_warning
+    check_rand_strat_warning = check_rand_strat_warning
   )
 }
 
@@ -243,7 +243,7 @@ robin_surv_strata <- function(
   exp_level,
   control_level,
   contrast,
-  check_randomization_strata_warning = FALSE
+  check_rand_strat_warning = FALSE
 ) {
   robin_surv_comparison(
     score_fun = h_lr_score_strat,
@@ -257,7 +257,7 @@ robin_surv_strata <- function(
     status = vars$status,
     strata = vars$strata,
     randomization_strata = vars$randomization_strata,
-    check_randomization_strata_warning = check_randomization_strata_warning
+    check_rand_strat_warning = check_rand_strat_warning
   )
 }
 
@@ -516,7 +516,7 @@ robin_surv <- function(
   # randomization strata in the analysis model has already been required in a comparison.
   # We want to avoid checking for the need of a warning, or giving a warning,
   # multiple times.
-  give_randomization_strata_warning <- FALSE
+  give_rand_strat_warning <- FALSE
 
   estimates <- lapply(
     seq_len(n_comparisons),
@@ -529,19 +529,19 @@ robin_surv <- function(
         exp_level = exp_level,
         control_level = control_level,
         contrast = contrast,
-        check_randomization_strata_warning = !give_randomization_strata_warning,
+        check_rand_strat_warning = !give_rand_strat_warning,
         ...
       )
-      if (!give_randomization_strata_warning) {
+      if (!give_rand_strat_warning) {
         # Only update if we have checked for it in this iteration, otherwise
         # we could overwrite a TRUE value with the default FALSE.
-        give_randomization_strata_warning <<- result$give_randomization_strata_warning
+        give_rand_strat_warning <<- result$give_rand_strat_warning
       }
       result
     }
   )
 
-  if (give_randomization_strata_warning) {
+  if (give_rand_strat_warning) {
     missing_vars <- setdiff(input$randomization_strata, c(input$covariates, input$strata))
     cov_string <- if (length(missing_vars) > 1) {
       paste0("interaction(", toString(missing_vars), ")")

R/survival_score.R

@@ -53,7 +53,7 @@ h_lr_score_no_strata_no_cov <- function(
   n = nrow(df),
   use_ties_factor = TRUE,
   calculate_variance = TRUE,
-  check_randomization_strata_warning = FALSE
+  check_rand_strat_warning = FALSE
 ) {
   assert_numeric(theta, min.len = 1L, finite = TRUE)
   assert_data_frame(df)
@@ -69,10 +69,10 @@ h_lr_score_no_strata_no_cov <- function(
   assert_true(n >= nrow(df))
   assert_flag(use_ties_factor)
   assert_flag(calculate_variance)
-  assert_flag(check_randomization_strata_warning)
+  assert_flag(check_rand_strat_warning)
 
   # Check whether to warn about insufficient inclusion of randomization strata.
-  give_randomization_strata_warning <- (check_randomization_strata_warning &&
+  give_rand_strat_warning <- (check_rand_strat_warning &&
     length(randomization_strata) > 0)
 
   # Standardize data set format, subset to relevant variables.
@@ -141,7 +141,7 @@ h_lr_score_no_strata_no_cov <- function(
     sigma_l2 = sigma_l2,
     se_theta_l = se_theta_l,
     n = n,
-    give_randomization_strata_warning = give_randomization_strata_warning
+    give_rand_strat_warning = give_rand_strat_warning
   )
 }
 
@@ -156,7 +156,7 @@ h_lr_score_strat <- function(
   randomization_strata = character(),
   use_ties_factor = TRUE,
   calculate_variance = TRUE,
-  check_randomization_strata_warning = FALSE
+  check_rand_strat_warning = FALSE
 ) {
   assert_string(treatment)
   assert_string(time)
@@ -166,9 +166,9 @@ h_lr_score_strat <- function(
   lapply(df[strata], assert_factor)
   lapply(df[randomization_strata], assert_factor)
   assert_flag(calculate_variance)
-  assert_flag(check_randomization_strata_warning)
+  assert_flag(check_rand_strat_warning)
 
-  give_randomization_strata_warning <- if (check_randomization_strata_warning) {
+  give_rand_strat_warning <- if (check_rand_strat_warning) {
     length(randomization_strata) > 0 &&
       !all(randomization_strata %in% strata) &&
       !h_are_factors_nested(interaction(df[strata]), interaction(df[randomization_strata]))
@@ -193,7 +193,7 @@ h_lr_score_strat <- function(
     randomization_strata = randomization_strata,
     use_ties_factor = use_ties_factor,
     calculate_variance = calculate_variance,
-    check_randomization_strata_warning = FALSE
+    check_rand_strat_warning = FALSE
   )
 
   u_sl <- sum_vectors_in_list(strata_results)
@@ -204,7 +204,7 @@ h_lr_score_strat <- function(
     sigma_l2 = sigma_sl2,
     se_theta_l = se_theta_sl,
     n = n,
-    give_randomization_strata_warning = give_randomization_strata_warning
+    give_rand_strat_warning = give_rand_strat_warning
   )
 }
 
@@ -221,7 +221,7 @@ h_lr_score_cov <- function(
   use_ties_factor = TRUE,
   hr_se_plugin_adjusted = TRUE,
   calculate_variance = TRUE,
-  check_randomization_strata_warning = FALSE
+  check_rand_strat_warning = FALSE
 ) {
   assert_data_frame(df)
   assert_string(treatment)
@@ -234,7 +234,7 @@ h_lr_score_cov <- function(
   assert_factor(df[[treatment]], n.levels = 2L, any.missing = FALSE)
   assert_flag(hr_se_plugin_adjusted)
   assert_flag(calculate_variance)
-  assert_flag(check_randomization_strata_warning)
+  assert_flag(check_rand_strat_warning)
 
   # Subset to complete records here.
   required_cols <- unique(c(treatment, time, status, covariates, randomization_strata))
@@ -256,7 +256,7 @@ h_lr_score_cov <- function(
   lm_results <- h_get_lm_results(lm_input)
   beta_est <- lm_results$beta_est
 
-  give_randomization_strata_warning <- if (check_randomization_strata_warning) {
+  give_rand_strat_warning <- if (check_rand_strat_warning) {
     length(randomization_strata) > 0 &&
       !h_unbiased_means_across_strata(
         residuals_per_group = lm_results$residuals,
@@ -278,7 +278,7 @@ h_lr_score_cov <- function(
     randomization_strata = randomization_strata,
     use_ties_factor = use_ties_factor,
     calculate_variance = calculate_variance,
-    check_randomization_strata_warning = FALSE
+    check_rand_strat_warning = FALSE
   )
 
   # We assume here that the observed proportion of treatment 1 in the data set corresponds to the preplanned
@@ -314,7 +314,7 @@ h_lr_score_cov <- function(
         randomization_strata = randomization_strata,
         use_ties_factor = use_ties_factor,
         calculate_variance = TRUE,
-        check_randomization_strata_warning = FALSE
+        check_rand_strat_warning = FALSE
       )
       attr(unadj_score_theta_hat, "sigma_l2")
     }
@@ -335,7 +335,7 @@ h_lr_score_cov <- function(
     se_theta_l = if (calculate_variance) se_theta_cl else NA_real_,
     sigma_l2 = if (calculate_variance) sigma_cl2 else NA_real_,
     n = n,
-    give_randomization_strata_warning = give_randomization_strata_warning
+    give_rand_strat_warning = give_rand_strat_warning
   )
 }
 
@@ -353,7 +353,7 @@ h_lr_score_strat_cov <- function(
   use_ties_factor = TRUE,
   hr_se_plugin_adjusted = TRUE,
   calculate_variance = TRUE,
-  check_randomization_strata_warning = FALSE
+  check_rand_strat_warning = FALSE
 ) {
   assert_data_frame(df)
   assert_string(treatment)
@@ -366,7 +366,7 @@ h_lr_score_strat_cov <- function(
   assert_subset(c(treatment, time, status, strata, covariates), names(df))
   assert_flag(hr_se_plugin_adjusted)
   assert_flag(calculate_variance)
-  assert_flag(check_randomization_strata_warning)
+  assert_flag(check_rand_strat_warning)
 
   # Subset to complete records here.
   required_cols <- unique(c(treatment, time, status, covariates, strata, randomization_strata))
@@ -397,7 +397,7 @@ h_lr_score_strat_cov <- function(
   lm_results <- h_get_strat_lm_results(strat_lm_input)
   beta_est <- lm_results$beta_est
 
-  give_randomization_strata_warning <- if (check_randomization_strata_warning) {
+  give_rand_strat_warning <- if (check_rand_strat_warning) {
     length(randomization_strata) > 0 &&
       !h_unbiased_means_across_strata(
         residuals_per_group = lm_results$residuals,
@@ -419,7 +419,7 @@ h_lr_score_strat_cov <- function(
     randomization_strata,
     use_ties_factor = use_ties_factor,
     calculate_variance = calculate_variance,
-    check_randomization_strata_warning = FALSE
+    check_rand_strat_warning = FALSE
   )
 
   # We assume here that the observed proportion of treatment 1 in the data set
@@ -481,7 +481,7 @@ h_lr_score_strat_cov <- function(
         randomization_strata = randomization_strata,
         use_ties_factor = use_ties_factor,
         calculate_variance = TRUE,
-        check_randomization_strata_warning = FALSE
+        check_rand_strat_warning = FALSE
       )
       attr(strat_unadj_score_theta_hat, "sigma_l2")
     }
@@ -497,6 +497,6 @@ h_lr_score_strat_cov <- function(
     se_theta_l = if (calculate_variance) se_theta_csl else NA_real_,
     sigma_l2 = if (calculate_variance) sigma_csl2 else NA_real_,
     n = n,
-    give_randomization_strata_warning = give_randomization_strata_warning
+    give_rand_strat_warning = give_rand_strat_warning
   )
 }

man/survival_comparison_functions.Rd

@@ -16,49 +16,49 @@
 # h_lr_test_via_score works as expected
 
     list(u_l = 0.0895537761860842, sigma_l2 = 0.17706769289317, tau_l = 3.21352484896035, 
-        pval = 0.0013111645203555, n = 228L, give_randomization_strata_warning = FALSE)
+        pval = 0.0013111645203555, n = 228L, give_rand_strat_warning = FALSE)
 
 # robin_surv_comparison works as expected without covariate adjustment
 
     list(estimate = 0.53342923961288, se = 0.167270504465807, hr_n = 228L, 
         hr_sigma_l2 = 0.156756810948865, test_stat = 3.21352484896035, 
         p_value = 0.0013111645203555, test_score = 0.0895537761860842, 
-        test_n = 228L, test_sigma_l2 = 0.17706769289317, give_randomization_strata_warning = FALSE)
+        test_n = 228L, test_sigma_l2 = 0.17706769289317, give_rand_strat_warning = FALSE)
 
 # robin_surv_comparison can skip the hazard ratio estimation
 
     list(estimate = NA_real_, se = NA_real_, hr_n = NA_integer_, 
         hr_sigma_l2 = NA_real_, test_stat = 3.21352484896035, p_value = 0.0013111645203555, 
         test_score = 0.0895537761860842, test_n = 228L, test_sigma_l2 = 0.17706769289317, 
-        give_randomization_strata_warning = FALSE)
+        give_rand_strat_warning = FALSE)
 
 # robin_surv_no_strata_no_cov works as expected
 
     list(estimate = -0.53342923961288, se = 0.167270504465807, hr_n = 228L, 
         hr_sigma_l2 = 0.156756810948865, test_stat = -3.21352484896035, 
         p_value = 0.0013111645203555, test_score = -0.0895537761860842, 
-        test_n = 228L, test_sigma_l2 = 0.17706769289317, give_randomization_strata_warning = FALSE)
+        test_n = 228L, test_sigma_l2 = 0.17706769289317, give_rand_strat_warning = FALSE)
 
 # robin_surv_strata works as expected
 
     list(estimate = -0.554820697243091, se = 0.170628735365298, hr_n = 227L, 
         hr_sigma_l2 = 0.151310755205329, test_stat = -3.2855836062253, 
         p_value = 0.00101771334472415, test_score = -0.0896871248297144, 
-        test_n = 227L, test_sigma_l2 = 0.169145720705825, give_randomization_strata_warning = FALSE)
+        test_n = 227L, test_sigma_l2 = 0.169145720705825, give_rand_strat_warning = FALSE)
 
 # robin_surv_cov works as expected
 
     list(estimate = -0.503821677843015, se = 0.1656799127954, hr_n = 228L, 
         hr_sigma_l2 = 0.156990532256188, test_stat = -3.06610317830763, 
         p_value = 0.0021686846372504, test_score = -0.0851266162073163, 
-        test_n = 228L, test_sigma_l2 = 0.175748674116587, give_randomization_strata_warning = FALSE)
+        test_n = 228L, test_sigma_l2 = 0.175748674116587, give_rand_strat_warning = FALSE)
 
 # robin_surv_strata_cov works as expected
 
     list(estimate = -0.0136495961352817, se = 0.158066245456898, 
         hr_n = 227L, hr_sigma_l2 = 0.172478508438683, test_stat = -0.0835488066000914, 
         p_value = 0.933415170960403, test_score = -0.00229971887336489, 
-        test_n = 227L, test_sigma_l2 = 0.171986696328211, give_randomization_strata_warning = FALSE)
+        test_n = 227L, test_sigma_l2 = 0.171986696328211, give_rand_strat_warning = FALSE)
 
 # h_log_hr_coef_mat works as expected