From 73e650114af77582238abf5273e63005e0b2287e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 6 Mar 2017 17:00:48 +0100 Subject: Static documentation now built by pkgdown::build_site() --- docs/massart97ex3.html | 194 ------------------------------------------------- 1 file changed, 194 deletions(-) delete mode 100644 docs/massart97ex3.html (limited to 'docs/massart97ex3.html') diff --git a/docs/massart97ex3.html b/docs/massart97ex3.html deleted file mode 100644 index 7e4d510..0000000 --- a/docs/massart97ex3.html +++ /dev/null @@ -1,194 +0,0 @@ - - - - -massart97ex3. chemCal 0.1-37 - - - - - - - - - - - - - - - - - - -
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Calibration data from Massart et al. (1997), example 3

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Usage

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data(massart97ex3)
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Description

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Sample dataset from p. 188 to test the package.

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Format

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

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Source

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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.

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Examples

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data(massart97ex3) -attach(massart97ex3)
-The following objects are masked from massart97ex3 (pos = 3): - - x, y - -
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)] -m <- lm(y ~ x, w = weights) -calplot(m)
-Warning message: -Assuming constant prediction variance even though model fit is weighted - -

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-# The following concords with the book p. 200 -inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
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$Prediction -[1] 5.865367 - -$`Standard Error` -[1] 0.8926109 - -$Confidence -[1] 2.478285 - -$`Confidence Limits` -[1] 3.387082 8.343652 - -
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inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
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$Prediction -[1] 44.06025 - -$`Standard Error` -[1] 2.829162 - -$Confidence -[1] 7.855012 - -$`Confidence Limits` -[1] 36.20523 51.91526 - -
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-# The LOD is only calculated for models from unweighted regression -# with this version of chemCal -m0 <- lm(y ~ x) -lod(m0)
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$x -[1] 5.407085 - -$y - 1 -13.63911 - -
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-# Limit of quantification from unweighted regression -loq(m0)
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$x -[1] 13.97764 - -$y - 1 -30.6235 - -
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-# 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(m, w.loq = 1.67)
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$x -[1] 7.346195 - -$y - 1 -17.90777 - -
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# 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.
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