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$Confidence, 7), 3.168489)
})
test_that("Inverse predictions for weighted regressions are stable", {
weights <- with(massart97ex3, {
yx <- split(y, x)
ybar <- sapply(yx, mean)
s <- round(sapply(yx, sd), digits = 2)
w <- round(1 / (s^2), digits = 3)
})
massart97ex3.means <- aggregate(y ~ x, massart97ex3, mean)
m3.means <- lm(y ~ x, w = weights, data = massart97ex3.means)
p3.1 <- inverse.predict(m3.means, 15, ws = 1.67)
expect_equal(signif(p3.1$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.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)
})
