Fix tests

paulgovan · paulgovan · commit a6db73df6ede · 2025-10-12T19:58:48.000-04:00
diff --git a/.Rproj.user/CB2BA49E/pcs/files-pane.pper b/.Rproj.user/CB2BA49E/pcs/files-pane.pper
@@ -5,5 +5,5 @@
             "ascending": true
         }
     ],
-    "path": "~/Documents/ReliaGrowR/tests/testthat"
+    "path": "~/Documents/ReliaGrowR/R"
 }
diff --git a/R/duane.R b/R/duane.R
@@ -1,4 +1,3 @@
-
 #' Duane Analysis
 #'
 #' This function performs a Duane analysis (1962) <doi:10.1109/TA.1964.4319640>
@@ -52,8 +51,7 @@
 #' @srrstats {G5.9a} Unit tests check that adding trivial noise to data does not meaningfully change results.
 #' @srrstats {G5.9b} Unit tests check that different random seeds do not meaningfully change results.
 #' @srrstats {G5.10} All unit tests run as part of continuous integration.
-#' @srrstats {RE1.2] Documentation includes expected format for inputting predictor variables
-#' (`times`, `failures`).
+#' @srrstats {RE1.2} Documentation includes expected format for inputting predictor variables (`times`, `failures`).
 #' @srrstats {RE1.3} Output retains all relevant aspects of input data.
 #' @srrstats {RE1.3a} Output retains all relevant aspects of input data.
 #' @srrstats {RE1.4} Documentation includes assumptions for the input data (i.e., positive, finite values).
diff --git a/R/gof.R b/R/gof.R
@@ -1,4 +1,3 @@
-
 #' Q-Q Plot for RGA Objects
 #'
 #' This function creates a Q-Q plot for a fitted Reliability Growth Analysis (RGA) model
@@ -149,5 +148,3 @@ ppplot.rga <- function(x,
   # 45-degree reference line
   abline(lsfit(theo_cdf, emp_cdf))
 }
-
-
diff --git a/R/rdt.R b/R/rdt.R
@@ -1,4 +1,3 @@
-
 #' Reliability Demonstration Test (RDT) Plan Calculator
 #'
 #' This function calculates the required test time or sample size for a Reliability
diff --git a/R/rga.R b/R/rga.R
@@ -1,4 +1,3 @@
-
 #' Reliability Growth Analysis.
 #'
 #' This function performs reliability growth analysis using the Crow-AMSAA model by
@@ -58,8 +57,7 @@
 #' @srrstats {G5.9a} Unit tests check that adding trivial noise to data does not meaningfully change results.
 #' @srrstats {G5.9b} Unit tests check that different random seeds do not meaningfully change results.
 #' @srrstats {G5.10} All unit tests run as part of continuous integration.
-#' @srrstats {RE1.2] Documentation includes expected format for inputting predictor variables
-#' (`times`, `failures`).
+#' @srrstats {RE1.2} Documentation includes expected format for inputting predictor variables (`times`, `failures`).
 #' @srrstats {RE1.3} Output retains all relevant aspects of input data.
 #' @srrstats {RE1.3a} Output retains all relevant aspects of input data.
 #' @srrstats {RE1.4} Documentation includes assumptions for the input data (i.e., positive, finite values).
@@ -93,7 +91,7 @@
 #' @srrstats {RE7.2} Unit tests demonstrate that output objects retain aspects
 #' of input data such as case names.
 #' @srrstats {RE7.3} Unit tests demonstrate expected behavior when `rga` object
-#'is submitted to the accessor methods `print` and `plot`.
+#' is submitted to the accessor methods `print` and `plot`.
 #'
 #' @param times Either a numeric vector of cumulative failure times or a data frame
 #' containing both failure times and failure counts. If a data frame is provided, it must
@@ -228,7 +226,7 @@ rga <- function(times, failures, model_type = "Crow-AMSAA", breaks = NULL, conf_
   # Check for perfect collinearity
   cor_val <- suppressWarnings(cor(log_times, log_cum_failures))
   if (is.na(cor_val) || abs(cor_val - 1) < .Machine$double.eps^0.5 ||
-      abs(cor_val + 1) < .Machine$double.eps^0.5) {
+    abs(cor_val + 1) < .Machine$double.eps^0.5) {
     stop("Perfect collinearity detected between predictor ('log_times') and response ('log_cum_failures'). Regression cannot be performed.")
   }
 
diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R
@@ -1,4 +1,3 @@
-
 #' NA_standards
 #'
 #' @srrstatsNA {G1.4a} There are no internal (non-exported) functions.
diff --git a/R/testdata.r b/R/testdata.r
@@ -1,4 +1,3 @@
-
 #' Reliability Test Data
 #'
 #' A dataset containing example reliability test data from the military report
diff --git a/R/weibull_to_rga.R b/R/weibull_to_rga.R
@@ -1,4 +1,3 @@
-
 #' Weibull to RGA
 #'
 #' Converts Weibull data (failure, suspension, and interval-censored times)
@@ -144,5 +143,3 @@ weibull_to_rga <- function(failures,
 
   return(result)
 }
-
-
diff --git a/data-raw/TESTDATA.R b/data-raw/TESTDATA.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.0} The data set is a standard data set from a published paper.
 #' @srrstats {G5.1} The data set is created within and used to test the package.
 #' The data set is exported so that users can confirm tests and run examples.
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
diff --git a/tests/testthat/test-srr-duane.R b/tests/testthat/test-srr-duane.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.1} The function is tested with a standard data set. The data set is
 #' created within and used to test the package. The data set is exported so that users
 #' can confirm tests and run examples.
@@ -27,7 +26,7 @@
 #' @srrstats {RE7.2} Unit tests demonstrate that output objects retain aspects
 #' of input data such as case names.
 #' @srrstats {RE7.3} Unit tests demonstrate expected behavior when `duane` object
-#'is submitted to the accessor methods `print` and `plot`.
+#' is submitted to the accessor methods `print` and `plot`.
 
 set.seed(123)
 
@@ -420,7 +419,7 @@ test_that("duane() handles noiseless, exact relationships efficiently", {
   })[["elapsed"]]
 
   # Check that noiseless fit is at least as fast or faster
-  expect_lte(t_noiseless, t_noisy * 1.05)  # small tolerance for variation
+  expect_lte(t_noiseless, t_noisy * 1.05) # small tolerance for variation
 
   # Check that coefficients are nearly identical in noiseless case
   coef_noiseless <- coef(fit_noiseless$model)
diff --git a/tests/testthat/test-srr-gof.R b/tests/testthat/test-srr-gof.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.2} Unit tests demonstrate error messages and compare results with expected values.
 #' @srrstats {G5.2a} Every message produced by `stop()` is unique.
 #' @srrstats {G5.2b} Unit tests demonstrate error messages and compare results with expected values.
@@ -55,17 +54,19 @@ test_that("ppplot.rga() errors on invalid inputs", {
 make_rga_numeric <- function() {
   times <- c(5, 10, 15, 20, 25)
   failures <- c(1, 2, 1, 3, 2)
-  rga(times, failures)  # assumes your rga() is available
+  rga(times, failures) # assumes your rga() is available
 }
 
 make_rga_matrix <- function() {
   fit <- make_rga_numeric()
   # Overwrite betas and lambdas to matrix/list form to cover branches
   fit$betas <- list(log_times = matrix(
-    c(0.9, 0.1), nrow = 1, dimnames = list(NULL, c("Est.", "SE"))
+    c(0.9, 0.1),
+    nrow = 1, dimnames = list(NULL, c("Est.", "SE"))
   ))
   fit$lambdas <- matrix(
-    c(0.002, 0.0005), nrow = 1, dimnames = list(NULL, c("Est.", "SE"))
+    c(0.002, 0.0005),
+    nrow = 1, dimnames = list(NULL, c("Est.", "SE"))
   )
   fit
 }
@@ -91,4 +92,3 @@ test_that("ppplot.rga runs silently on valid input (matrix/list params)", {
   fit <- make_rga_matrix()
   expect_silent(ppplot.rga(fit))
 })
-
diff --git a/tests/testthat/test-srr-rga.R b/tests/testthat/test-srr-rga.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.0} The function is tested with a standard data set from a published paper.
 #' @srrstats {G5.1} The function is tested with a standard data set. The data set is
 #' created within and used to test the package. The data set is exported so that users
@@ -28,7 +27,7 @@
 #' @srrstats {RE7.2} Unit tests demonstrate that output objects retain aspects
 #' of input data such as case names.
 #' @srrstats {RE7.3} Unit tests demonstrate expected behavior when `rga` object
-#'is submitted to the accessor methods `print` and `plot`.
+#' is submitted to the accessor methods `print` and `plot`.
 
 test_that("data.frame input works the same as separate vectors", {
   times <- c(100, 200, 300)
@@ -583,15 +582,14 @@ test_that("rga() fits near-noiseless data and is at least as fast as noisy data
     fit_noisy <- rga(times = rep(1, n), failures = failures_noisy, model_type = "Crow-AMSAA")
   })[["elapsed"]]
 
-  # Require near-noiseless to be <= 1.2 * noisy (allow CI variability)
+  # Require near-noiseless to be <= 1.05 * noisy (allow CI variability)
   expect_true(
     t_near <= t_noisy * 1.05 + 0.01,
-    info = sprintf("Noiseless fit took %.3fs vs noisy %.3fs", t_noiseless, t_noisy)
+    info = sprintf("Noiseless fit took %.3fs vs noisy %.3fs", t_near, t_noisy)
   )
 })
 
 test_that("rga() fits near-noiseless data with user-supplied breaks (Piecewise NHPP) and is reasonably fast", {
-
   set.seed(101)
   n <- 400
   cum_time <- seq(1, n)
@@ -601,7 +599,7 @@ test_that("rga() fits near-noiseless data with user-supplied breaks (Piecewise N
   failures <- diff(c(0, cum_failures))
 
   # near-noiseless
-  near_noise <- rnorm(n, mean = 0, sd = 5e-3)   # changed from 1e-4 to 5e-3
+  near_noise <- rnorm(n, mean = 0, sd = 5e-3) # changed from 1e-4 to 5e-3
   cum_failures_near <- cum_failures * exp(near_noise)
   failures_near <- diff(c(0, cum_failures_near))
   failures_near <- pmax(failures_near, .Machine$double.eps)
@@ -615,17 +613,24 @@ test_that("rga() fits near-noiseless data with user-supplied breaks (Piecewise N
   break_pt <- floor(n / 2)
 
   t_near_pw <- system.time({
-    fit_near_pw <- rga(times = rep(1, n), failures = failures_near,
-                       model_type = "Piecewise NHPP", breaks = break_pt)
+    fit_near_pw <- rga(
+      times = rep(1, n), failures = failures_near,
+      model_type = "Piecewise NHPP", breaks = break_pt
+    )
   })[["elapsed"]]
 
   t_noisy_pw <- system.time({
-    fit_noisy_pw <- rga(times = rep(1, n), failures = failures_noisy,
-                        model_type = "Piecewise NHPP", breaks = break_pt)
+    fit_noisy_pw <- rga(
+      times = rep(1, n), failures = failures_noisy,
+      model_type = "Piecewise NHPP", breaks = break_pt
+    )
   })[["elapsed"]]
 
-  # Allow bigger slack because segmented fitting can vary across environments
-  expect_lte(t_near_pw, t_noisy_pw * 1.3)
+  # Require near-noiseless to be <= 1.05 * noisy (allow CI variability)
+  expect_true(
+    t_near_pw <= t_noisy_pw * 1.05 + 0.01,
+    info = sprintf("Noiseless fit took %.3fs vs noisy %.3fs", t_near_pw, t_noisy_pw)
+  )
 })
 
 test_that("rga output retains row / case names from input", {
@@ -650,4 +655,3 @@ test_that("rga output retains row / case names from input", {
   expect_equal(names(res_piecewise$times), names(times2))
   expect_equal(names(res_piecewise$failures), names(failures2))
 })
-
diff --git a/tests/testthat/test-srr-testdata.R b/tests/testthat/test-srr-testdata.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.1} The data set is created within and used to test the package.
 #' The data set is exported so that users can confirm tests and run examples.
 #' @srrstats {G5.10} All unit tests run as part of continuous integration.
diff --git a/tests/testthat/test-srr-weibull_to_rga.R b/tests/testthat/test-srr-weibull_to_rga.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.2} Unit tests demonstrate error messages and compare results with expected values.
 #' @srrstats {G5.2a} Every message produced by `stop()` is unique.
 #' @srrstats {G5.2b} Unit tests demonstrate error messages and compare results with expected values.
diff --git a/tests/testthat/test-srr_rdt.R b/tests/testthat/test-srr_rdt.R
@@ -1,4 +1,3 @@
-
 #' @srrstats {G5.2} Unit tests demonstrate error messages and compare results with expected values.
 #' @srrstats {G5.2a} Every message produced by `stop()` is unique.
 #' @srrstats {G5.2b} Unit tests demonstrate error messages and compare results with expected values.
@@ -204,8 +203,10 @@ test_that("RDT with test time input is stable to trivial noise", {
 })
 
 test_that("print.rdt works for required test time", {
-  plan <- rdt(target = 0.9, mission_time = 1000,
-              conf_level = 0.9, beta = 1, n = 10)
+  plan <- rdt(
+    target = 0.9, mission_time = 1000,
+    conf_level = 0.9, beta = 1, n = 10
+  )
 
   # Ensure output contains expected lines
   expect_output(print(plan), "Reliability Demonstration Test \\(RDT\\) Plan")
@@ -221,8 +222,10 @@ test_that("print.rdt works for required test time", {
 })
 
 test_that("print.rdt works for required sample size", {
-  plan <- rdt(target = 0.9, mission_time = 1000,
-              conf_level = 0.9, beta = 1, test_time = 2000)
+  plan <- rdt(
+    target = 0.9, mission_time = 1000,
+    conf_level = 0.9, beta = 1, test_time = 2000
+  )
 
   expect_output(print(plan), "Reliability Demonstration Test \\(RDT\\) Plan")
   expect_output(print(plan), "Distribution:")
@@ -239,4 +242,3 @@ test_that("print.rdt errors on wrong input", {
   expect_error(print.rdt(123), "'x' must be an object of class 'rdt'")
   expect_error(print.rdt(list()), "'x' must be an object of class 'rdt'")
 })
-

Original file line number	Diff line number	Diff line change
`@@ -5,5 +5,5 @@`
`5`	`5`	`"ascending": true`
`6`	`6`	`}`
`7`	`7`	`],`
`8`		`- "path": "~/Documents/ReliaGrowR/tests/testthat"`
	`8`	`+ "path": "~/Documents/ReliaGrowR/R"`
`9`	`9`	`}`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,3 @@`
`1`		`-`
`2`	`1`	`#' Reliability Demonstration Test (RDT) Plan Calculator`
`3`	`2`	`#'`
`4`	`3`	`#' This function calculates the required test time or sample size for a Reliability`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,3 @@`
`1`		`-`
`2`	`1`	`#' NA_standards`
`3`	`2`	`#'`
`4`	`3`	`#' @srrstatsNA {G1.4a} There are no internal (non-exported) functions.`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,3 @@`
`1`		`-`
`2`	`1`	`#' Reliability Test Data`
`3`	`2`	`#'`
`4`	`3`	`#' A dataset containing example reliability test data from the military report`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,3 @@`
`1`		`-`
`2`	`1`	`#' Weibull to RGA`
`3`	`2`	`#'`
`4`	`3`	`#' Converts Weibull data (failure, suspension, and interval-censored times)`
`@@ -144,5 +143,3 @@ weibull_to_rga <- function(failures,`
`144`	`143`
`145`	`144`	`return(result)`
`146`	`145`	`}`
`147`		`-`
`148`		`-`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,3 @@`
`1`		`-`
`2`	`1`	`#' @srrstats {G5.0} The data set is a standard data set from a published paper.`
`3`	`2`	`#' @srrstats {G5.1} The data set is created within and used to test the package.`
`4`	`3`	`#' The data set is exported so that users can confirm tests and run examples.`