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authorranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2006-06-23 15:33:27 +0000
committerranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2006-06-23 15:33:27 +0000
commit9e0dae397df8c18e7333d2604cae96b2a7927420 (patch)
treeb513b791985426bab6c18850d2f8c308c411c1a5 /tests
parentfb7ea47c774f67b8c26a6844f4ade8935a8653cc (diff)
- inverse.predict now has a var.s argument instead of the never
tested ss argument. This is documented in the updated vignette - loq() now has w.loq and var.loq arguments, and stops with a message if neither are specified and the model has weights. - calplot doesn't stop any more for weighted regression models, but only refrains from drawing prediction bands - Added method = "din" to lod(), now that I actually have it (DIN 32645) and was able to see which approximation is used therein. - removed the demos, as the examples and tests are already partially duplicated - The vignette is more of a collection of various notes, but should certainly be helpful for the user. - Version bump to 0.1-xxx git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@16 5fad18fb-23f0-0310-ab10-e59a3bee62b4
Diffstat (limited to 'tests')
-rw-r--r--tests/din32645.R6
-rw-r--r--tests/din32645.Rout.save10
-rw-r--r--tests/massart97.R25
-rw-r--r--tests/massart97.Rout.save47
4 files changed, 62 insertions, 26 deletions
diff --git a/tests/din32645.R b/tests/din32645.R
index dc0aee6..e5ffed7 100644
--- a/tests/din32645.R
+++ b/tests/din32645.R
@@ -1,7 +1,7 @@
-library(chemCal)
+require(chemCal)
data(din32645)
-m <- lm(y ~ x, data=din32645)
-inverse.predict(m,3500,alpha=0.01)
+m <- lm(y ~ x, data = din32645)
+inverse.predict(m, 3500, alpha = 0.01)
lod <- lod(m, alpha = 0.01, beta = 0.5)
lod(m, alpha = 0.01, beta = 0.01)
loq <- loq(m, alpha = 0.01)
diff --git a/tests/din32645.Rout.save b/tests/din32645.Rout.save
index 10cd1ab..c5ed5a7 100644
--- a/tests/din32645.Rout.save
+++ b/tests/din32645.Rout.save
@@ -1,6 +1,6 @@
R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.0 (2006-04-24)
+Version 2.3.1 (2006-06-01)
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
@@ -15,10 +15,12 @@ Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
-> library(chemCal)
+> require(chemCal)
+Loading required package: chemCal
+[1] TRUE
> data(din32645)
-> m <- lm(y ~ x, data=din32645)
-> inverse.predict(m,3500,alpha=0.01)
+> m <- lm(y ~ x, data = din32645)
+> inverse.predict(m, 3500, alpha = 0.01)
$Prediction
[1] 0.1054792
diff --git a/tests/massart97.R b/tests/massart97.R
index 7170ec4..58119d9 100644
--- a/tests/massart97.R
+++ b/tests/massart97.R
@@ -1,12 +1,19 @@
-library(chemCal)
+require(chemCal)
data(massart97ex3)
attach(massart97ex3)
-yx <- split(y,x)
-ybar <- sapply(yx,mean)
-s <- round(sapply(yx,sd),digits=2)
-w <- round(1/(s^2),digits=3)
+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)
-# The following concords with the book
-inverse.predict(m, 15, ws = 1.67)
-inverse.predict(m, 90, ws = 0.145)
+m <- lm(y ~ x, w = weights)
+#calplot(m)
+
+inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
+inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
+
+m0 <- lm(y ~ x)
+lod(m0)
+
+loq(m0)
+loq(m, w.loq = 1.67)
diff --git a/tests/massart97.Rout.save b/tests/massart97.Rout.save
index ae50275..9386a11 100644
--- a/tests/massart97.Rout.save
+++ b/tests/massart97.Rout.save
@@ -1,6 +1,6 @@
R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.0 (2006-04-24)
+Version 2.3.1 (2006-06-01)
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
@@ -15,17 +15,20 @@ Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
-> library(chemCal)
+> require(chemCal)
+Loading required package: chemCal
+[1] TRUE
> data(massart97ex3)
> attach(massart97ex3)
-> yx <- split(y,x)
-> ybar <- sapply(yx,mean)
-> s <- round(sapply(yx,sd),digits=2)
-> w <- round(1/(s^2),digits=3)
+> 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)
-> # The following concords with the book
-> inverse.predict(m, 15, ws = 1.67)
+> m <- lm(y ~ x, w = weights)
+> #calplot(m)
+>
+> inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
$Prediction
[1] 5.865367
@@ -38,7 +41,7 @@ $Confidence
$`Confidence Limits`
[1] 3.387082 8.343652
-> inverse.predict(m, 90, ws = 0.145)
+> inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
$Prediction
[1] 44.06025
@@ -52,3 +55,27 @@ $`Confidence Limits`
[1] 36.20523 51.91526
>
+> m0 <- lm(y ~ x)
+> lod(m0)
+$x
+[1] 5.406637
+
+$y
+[1] 13.63822
+
+>
+> loq(m0)
+$x
+[1] 13.97767
+
+$y
+[1] 30.62355
+
+> loq(m, w.loq = 1.67)
+$x
+[1] 7.346231
+
+$y
+[1] 17.90784
+
+>

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