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/reference/massart97ex3.html | 188 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 188 insertions(+) create mode 100644 docs/reference/massart97ex3.html (limited to 'docs/reference/massart97ex3.html') diff --git a/docs/reference/massart97ex3.html b/docs/reference/massart97ex3.html new file mode 100644 index 0000000..2ec8f6f --- /dev/null +++ b/docs/reference/massart97ex3.html @@ -0,0 +1,188 @@ + + + + + + + + +Calibration data from Massart et al. (1997), example 3 — massart97ex3 • chemCal + + + + + + + + + + + + + + + + + + + + + + + + +
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Calibration data from Massart et al. (1997), example 3

+
+ + +

Sample dataset from p. 188 to test the package.

+ + + 
data(massart97ex3)
+ +

Format

+ +

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

+ +

Source

+ +

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.

+ + +

Examples

+ 
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: Assuming constant prediction variance even though model fit is weighted

+# The following concords with the book p. 200
+inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
#> $Prediction
+#> [1] 5.865367
+#> 
+#> $`Standard Error`
+#> [1] 0.8926109
+#> 
+#> $Confidence
+#> [1] 2.478285
+#> 
+#> $`Confidence Limits`
+#> [1] 3.387082 8.343652
+#> 
inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
#> $Prediction
+#> [1] 44.06025
+#> 
+#> $`Standard Error`
+#> [1] 2.829162
+#> 
+#> $Confidence
+#> [1] 7.855012
+#> 
+#> $`Confidence Limits`
+#> [1] 36.20523 51.91526
+#> 

+# The LOD is only calculated for models from unweighted regression
+# with this version of chemCal
+m0 <- lm(y ~ x)
+lod(m0)
#> $x
+#> [1] 5.407085
+#> 
+#> $y
+#>        1 
+#> 13.63911 
+#> 

+# Limit of quantification from unweighted regression
+loq(m0)
#> $x
+#> [1] 13.97764
+#> 
+#> $y
+#>       1 
+#> 30.6235 
+#> 

+# 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)
#> $x
+#> [1] 7.346195
+#> 
+#> $y
+#>        1 
+#> 17.90777 
+#> 
# 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.
+
+ +
+ + +
+ + + -- cgit v1.2.1