From 9411139beee167c5339e96db448e5dbed19e06bc Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Thu, 5 Jul 2018 18:44:22 +0200
Subject: Maintenance in preparation of improvements

- Switch vignette to html
- Switch tests to testthat
- NEWS.md instead of ChangeLog
- Remove names of y in lists returned by lod and loq
---
 tests/testthat/test_din32645.R        | 33 +++++++++++++++++++++++++++
 tests/testthat/test_inverse.predict.R | 43 +++++++++++++++++++++++++++++++++++
 tests/testthat/test_lod_loq.R         | 28 +++++++++++++++++++++++
 tests/testthat/test_massart.R         | 40 ++++++++++++++++++++++++++++++++
 4 files changed, 144 insertions(+)
 create mode 100644 tests/testthat/test_din32645.R
 create mode 100644 tests/testthat/test_inverse.predict.R
 create mode 100644 tests/testthat/test_lod_loq.R
 create mode 100644 tests/testthat/test_massart.R

(limited to 'tests/testthat')

diff --git a/tests/testthat/test_din32645.R b/tests/testthat/test_din32645.R
new file mode 100644
index 0000000..e6d2840
--- /dev/null
+++ b/tests/testthat/test_din32645.R
@@ -0,0 +1,33 @@
+context("Known results for the dataset provided in DIN 32645")
+
+require(chemCal)
+
+m <- lm(y ~ x, data = din32645)
+prediction <- inverse.predict(m, 3500, alpha = 0.01)
+
+test_that("We get correct confidence intervals", {
+  # Result collected from Procontrol 3.1 (isomehr GmbH)
+  expect_equal(round(prediction$Confidence, 5), 0.07434)
+})
+
+test_that("We get a correct critical value", {
+  crit <- lod(m, alpha = 0.01, beta = 0.5)
+  # DIN 32645 gives 0.07 for the critical value
+  # (decision limit, "Nachweisgrenze")
+  expect_equal(round(crit$x, 2), 0.07)
+  # According to Dintest test data, we should get 0.0698
+  expect_equal(round(crit$x, 4), 0.0698)
+})
+
+test_that("We get a correct smalles detectable value using the DIN method", {
+  lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
+  # DIN 32645 gives 0.14 for the smallest detectable value ("Erfassungsgrenze")
+  expect_equal(round(lod.din$x, 2), 0.14)
+})
+
+test_that("We get a correct limit of quantification", {
+  loq.din <- loq(m, alpha = 0.01)
+  # The value cited for Procontrol 3.1 (0.2121) deviates
+  # at the last digit, so we only test for three digits
+  expect_equal(round(loq.din$x, 3), 0.212)
+})
diff --git a/tests/testthat/test_inverse.predict.R b/tests/testthat/test_inverse.predict.R
new file mode 100644
index 0000000..61484dc
--- /dev/null
+++ b/tests/testthat/test_inverse.predict.R
@@ -0,0 +1,43 @@
+context("Inverse predictions")
+
+library(chemCal)
+
+test_that("Inverse predictions for unweighted regressions are stable", {
+  m1 <- lm(y ~ x, data = massart97ex1)
+
+  # Known values from chemcal Version 0.1-37
+  p1.1 <- inverse.predict(m1, 15)
+  expect_equal(signif(p1.1$Prediction, 7), 6.09381)
+  expect_equal(signif(p1.1$`Standard Error`, 7), 1.767278)
+  expect_equal(signif(p1.1$Confidence, 7), 4.906751)
+
+  p1.2 <- inverse.predict(m1, 90)
+  expect_equal(signif(p1.2$Prediction, 7), 43.93983)
+  expect_equal(signif(p1.2$`Standard Error`, 7), 1.767747)
+  expect_equal(signif(p1.2$Confidence, 7), 4.908053)
+
+  p1.3 <- inverse.predict(m1, rep(90, 5))
+  expect_equal(signif(p1.3$Prediction, 7), 43.93983)
+  expect_equal(signif(p1.3$`Standard Error`, 7), 1.141204)
+  expect_equal(signif(p1.3$Confidence, 7), 3.168489)
+})
+
+test_that("Inverse predictions for weighted regressions are stable", {
+  attach(massart97ex3)
+  yx <- split(y, x)
+  ybar <- sapply(yx, mean)
+  s <- round(sapply(yx, sd), digits = 2)
+  w <- round(1 / (s^2), digits = 3)
+  weights <- w[factor(x)]
+  m3 <- lm(y ~ x, w = weights)
+
+  p3.1 <- inverse.predict(m3, 15, ws = 1.67)
+  expect_equal(signif(p3.1$Prediction, 7), 5.865367)
+  expect_equal(signif(p3.1$`Standard Error`, 7), 0.8926109)
+  expect_equal(signif(p3.1$Confidence, 7), 2.478285)
+
+  p3.2 <- inverse.predict(m3, 90, ws = 0.145)
+  expect_equal(signif(p3.2$Prediction, 7), 44.06025)
+  expect_equal(signif(p3.2$`Standard Error`, 7), 2.829162)
+  expect_equal(signif(p3.2$Confidence, 7), 7.855012)
+})
diff --git a/tests/testthat/test_lod_loq.R b/tests/testthat/test_lod_loq.R
new file mode 100644
index 0000000..6ba0ad0
--- /dev/null
+++ b/tests/testthat/test_lod_loq.R
@@ -0,0 +1,28 @@
+context("LOD and LOQ")
+
+library(chemCal)
+
+test_that("lod is stable across chemCal versions", {
+  m <- lm(y ~ x, data = din32645)
+  lod_1 <- lod(m)
+  expect_equal(signif(lod_1$x, 7), 0.08655484)
+  expect_equal(signif(lod_1$y, 7), 3317.154)
+
+  # Critical value (decision limit, Nachweisgrenze)
+  lod_2 <- lod(m, alpha = 0.01, beta = 0.5)
+  expect_equal(signif(lod_2$x, 7), 0.0698127)
+  expect_equal(signif(lod_2$y, 7), 3155.393)
+})
+
+test_that("loq is stable across chemCal versions", {
+  m2 <- lm(y ~ x, data = massart97ex3)
+  loq_1 <- loq(m2)
+  expect_equal(signif(loq_1$x, 7), 13.97764)
+  expect_equal(signif(loq_1$y, 7), 30.6235)
+
+  loq_2 <- loq(m2, n = 3)
+  expect_equal(signif(loq_2$x, 7), 9.971963)
+  expect_equal(signif(loq_2$y, 7), 22.68539)
+})
+
+
diff --git a/tests/testthat/test_massart.R b/tests/testthat/test_massart.R
new file mode 100644
index 0000000..791c2e7
--- /dev/null
+++ b/tests/testthat/test_massart.R
@@ -0,0 +1,40 @@
+context("Known results for the example datasets provided by Massart (1997)")
+
+require(chemCal)
+
+test_that("Inverse predictions for example 1 are correct",{
+  m1 <- lm(y ~ x, data = massart97ex1)
+
+  # Known values are from the book
+  p1.1 <- inverse.predict(m1, 15)
+  expect_equal(round(p1.1$Prediction, 1), 6.1)
+  expect_equal(round(p1.1$Confidence, 1), 4.9)
+
+  p1.2 <- inverse.predict(m1, 90)
+  expect_equal(round(p1.2$Prediction, 1), 43.9)
+  expect_equal(round(p1.2$Confidence, 1), 4.9)
+
+  p1.3 <- inverse.predict(m1, rep(90, 5))
+  expect_equal(round(p1.3$Prediction, 1), 43.9)
+  expect_equal(round(p1.3$Confidence, 1), 3.2)
+})
+
+
+test_that("Inverse predictions for example 3 are correct",{
+  attach(massart97ex3)
+  yx <- split(y, x)
+  ybar <- sapply(yx, mean)
+  s <- round(sapply(yx, sd), digits = 2)
+  w <- round(1 / (s^2), digits = 3)
+  weights <- w[factor(x)]
+  m3 <- lm(y ~ x, w = weights)
+
+  # Known values are from the book
+  p3.1 <- inverse.predict(m3, 15, ws = 1.67)
+  expect_equal(round(p3.1$Prediction, 1), 5.9)
+  expect_equal(round(p3.1$Confidence, 1), 2.5)
+
+  p3.2 <- inverse.predict(m3, 90, ws = 0.145)
+  expect_equal(round(p3.2$Prediction, 1), 44.1)
+  expect_equal(round(p3.2$Confidence, 1), 7.9)
+})
-- 
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