Description Usage Format Source Examples

Sample dataset from p. 188 to test the package.

1 |

A dataframe containing 6 levels of x values with 5 observations of y for each level.

Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, Chapter 8.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
# For reproducing the results for replicate standard measurements in example 8,
# we need to do the calibration on the means when using chemCal > 0.2
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)
# The following concords with the book p. 200
inverse.predict(m3.means, 15, ws = 1.67) # 5.9 +- 2.5
inverse.predict(m3.means, 90, ws = 0.145) # 44.1 +- 7.9
# The LOD is only calculated for models from unweighted regression
# with this version of chemCal
m0 <- lm(y ~ x, data = massart97ex3)
lod(m0)
# Limit of quantification from unweighted regression
loq(m0)
# For calculating the limit of quantification from a model from weighted
# regression, we need to supply weights, internally used for inverse.predict
# If we are not using a variance function, we can use the weight from
# the above example as a first approximation (x = 15 is close to our
# loq approx 14 from above).
loq(m3.means, w.loq = 1.67)
# The weight for the loq should therefore be derived at x = 7.3 instead
# of 15, but the graphical procedure of Massart (p. 201) to derive the
# variances on which the weights are based is quite inaccurate anyway.
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.