AssScalar: precision_ci_ul now available as metric

R/ass_scalar_helpers.R

@@ -108,6 +108,7 @@ get_metric <- function(
     metric_v <- numeric(length(ass_scalar$metrics))
     thresholds <- 1
     hr_and_more <- numeric(4) 
+    prec_ci <- numeric(2)
     if (!binary_bool) {
         thresholds <- unique(predicted)
         prevs <- vapply(thresholds, function(t) mean(predicted >= t), numeric(1))
@@ -195,11 +196,15 @@ get_metric <- function(
             },
             "precision_ci_ll" = {
                 check_one_threshold()
-                j_res <- stats::binom.test(
+                prec_ci <- stats::binom.test(
                     x = sum(actual[predicted >= thresholds]),
                     n = sum(predicted >= thresholds),
                     conf.level = ass_scalar$confidence_level
-                )$conf.int[1]
+                )$conf.int
+                j_res <- prec_ci[1]
+            },
+            "precision_ci_ul" = {
+                j_res <- prec_ci[2]
             },
             "hr" = {
                 check_one_threshold()

README.md

@@ -6,7 +6,7 @@
 
   <!-- badges: end -->
 
-## An R package for binary classification of survival
+## An R package pipelining binary classification of survival
 
 ### What's the scenario this R package is made for?
 

tests/testthat/test-ass_scalar.R

@@ -88,7 +88,8 @@ test_that("AssScalar$assess_center() works", {
   n_na_in_pheno <- 5
   n_fold <- 2
   multiple_metrics <- c("precision", "accuracy", "n_true", "perc_true", "n_samples", 
-    "logrank", "threshold", "prevalence", "precision_ci_ll", "hr", "hr_ci_ll", "hr_ci_ul", "hr_p")
+    "logrank", "threshold", "prevalence", "precision_ci_ll", "precision_ci_ul", "hr", "hr_ci_ll", 
+    "hr_ci_ul", "hr_p")
   single_metric <- "auc"
 
   dir <- withr::local_tempdir()
@@ -189,6 +190,9 @@ test_that("AssScalar$assess_center() works", {
   expect_equal(nrow(eval_tbl), length(model_list)-1)
   expect_equal(colnames(eval_tbl), c("model", "cohort", ass_scalar$metrics))
   expect_true(is.numeric(as.matrix(eval_tbl[, 3:ncol(eval_tbl)])))
+  expect_true(all(eval_tbl[["precision_ci_ll"]] < eval_tbl[["precision_ci_ul"]]))
+  expect_true(all(0 < eval_tbl[["precision_ci_ll"]]))
+  expect_true(all(eval_tbl[["precision_ci_ul"]] <= 1))
 
   data$cohort <- "test"
   ass_scalar$metrics <- "precision"