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-rw-r--r--tests/testthat/test_compare_investr.R30
-rw-r--r--tests/testthat/test_inverse.predict.R23
-rw-r--r--tests/testthat/test_lod_loq.R6
-rw-r--r--tests/testthat/test_massart.R22
4 files changed, 60 insertions, 21 deletions
diff --git a/tests/testthat/test_compare_investr.R b/tests/testthat/test_compare_investr.R
new file mode 100644
index 0000000..6191c89
--- /dev/null
+++ b/tests/testthat/test_compare_investr.R
@@ -0,0 +1,30 @@
+context("Compare with investr::calibrate")
+
+library(chemCal)
+library(investr)
+
+test_that("Unweighted regressions give same results as investr::calibrate using the Wald method", {
+ compare_investr <- function(object, y_sample) {
+ pred_chemCal <- inverse.predict(object, y_sample)
+ pred_investr <- calibrate(object, y_sample, interval = "Wald")
+ expect_equivalent(pred_chemCal[["Prediction"]],
+ pred_investr$estimate)
+ expect_equivalent(pred_chemCal[["Standard Error"]],
+ pred_investr$se)
+ expect_equivalent(pred_chemCal[["Confidence Limits"]][1],
+ pred_investr$lower)
+ expect_equivalent(pred_chemCal[["Confidence Limits"]][2],
+ pred_investr$upper)
+ }
+ m_tol <- lm(peak_area ~ amount, data = rl95_toluene)
+ compare_investr(m_tol, 1000)
+
+ m_din <- lm(y ~ x, din32645)
+ compare_investr(m_din, 5000)
+
+ m_m1 <- lm(y ~ x, massart97ex1)
+ compare_investr(m_m1, 15)
+
+ m_m3 <- lm(y ~ x, massart97ex3)
+ compare_investr(m_m3, 15)
+})
diff --git a/tests/testthat/test_inverse.predict.R b/tests/testthat/test_inverse.predict.R
index 61484dc..a4e1bfd 100644
--- a/tests/testthat/test_inverse.predict.R
+++ b/tests/testthat/test_inverse.predict.R
@@ -23,20 +23,23 @@ test_that("Inverse predictions for unweighted regressions are stable", {
})
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)
+ weights <- with(massart97ex3, {
+ yx <- split(y, x)
+ ybar <- sapply(yx, mean)
+ s <- round(sapply(yx, sd), digits = 2)
+ w <- round(1 / (s^2), digits = 3)
+ })
+
+ massart97ex3.means <- aggregate(y ~ x, massart97ex3, mean)
+
+ m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means)
+
+ p3.1 <- inverse.predict(m3.means, 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$Confidence, 7), 2.478285)
- p3.2 <- inverse.predict(m3, 90, ws = 0.145)
+ p3.2 <- inverse.predict(m3.means, 90, ws = 0.145)
expect_equal(signif(p3.2$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
index 6ba0ad0..da0f45e 100644
--- a/tests/testthat/test_lod_loq.R
+++ b/tests/testthat/test_lod_loq.R
@@ -15,7 +15,11 @@ test_that("lod is stable across chemCal versions", {
})
test_that("loq is stable across chemCal versions", {
- m2 <- lm(y ~ x, data = massart97ex3)
+ # Actually it was not stable between chemCal <0.2 and
+ # chemCal > 0.2, so we needed to adapt the test
+ # to work on a model on the means
+ massart97ex3_means <- aggregate(y ~ x, massart97ex3, mean)
+ m2 <- lm(y ~ x, data = massart97ex3_means)
loq_1 <- loq(m2)
expect_equal(signif(loq_1$x, 7), 13.97764)
expect_equal(signif(loq_1$y, 7), 30.6235)
diff --git a/tests/testthat/test_massart.R b/tests/testthat/test_massart.R
index 791c2e7..6e4ce75 100644
--- a/tests/testthat/test_massart.R
+++ b/tests/testthat/test_massart.R
@@ -19,22 +19,24 @@ test_that("Inverse predictions for example 1 are correct",{
expect_equal(round(p1.3$Confidence, 1), 3.2)
})
+test_that("Inverse predictions for example data 3 are correct when regressing on means",{
+ weights <- with(massart97ex3, {
+ yx <- split(y, x)
+ ybar <- sapply(yx, mean)
+ s <- round(sapply(yx, sd), digits = 2)
+ w <- round(1 / (s^2), digits = 3)
+ })
-test_that("Inverse predictions for example 3 are correct",{
- attach(massart97ex3)
- yx <- split(y, x)
- ybar <- sapply(yx, mean)
- s <- round(sapply(yx, sd), digits = 2)
- w <- round(1 / (s^2), digits = 3)
- weights <- w[factor(x)]
- m3 <- lm(y ~ x, w = weights)
+ massart97ex3.means <- aggregate(y ~ x, massart97ex3, mean)
+
+ m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means)
# Known values are from the book
- p3.1 <- inverse.predict(m3, 15, ws = 1.67)
+ p3.1 <- inverse.predict(m3.means, 15, ws = 1.67)
expect_equal(round(p3.1$Prediction, 1), 5.9)
expect_equal(round(p3.1$Confidence, 1), 2.5)
- p3.2 <- inverse.predict(m3, 90, ws = 0.145)
+ p3.2 <- inverse.predict(m3.means, 90, ws = 0.145)
expect_equal(round(p3.2$Prediction, 1), 44.1)
expect_equal(round(p3.2$Confidence, 1), 7.9)
})

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