Moved everything into the trunk directory, in order to enable branching

git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@22 5fad18fb-23f0-0310-ab10-e59a3bee62b4

4 files changed, 0 insertions, 200 deletions

diff --git a/tests/din32645.R b/tests/din32645.R
deleted file mode 100644
index e5ffed7..0000000
--- a/tests/din32645.R
+++ /dev/null
@@ -1,7 +0,0 @@
-require(chemCal)
-data(din32645)
-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
deleted file mode 100644
index c5ed5a7..0000000
--- a/tests/din32645.Rout.save
+++ /dev/null
@@ -1,45 +0,0 @@
-
-R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.1 (2006-06-01)
-ISBN 3-900051-07-0
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-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.
-
-> require(chemCal)
-Loading required package: chemCal
-[1] TRUE
-> data(din32645)
-> m <- lm(y ~ x, data = din32645)
-> inverse.predict(m, 3500, alpha = 0.01)
-$Prediction
-[1] 0.1054792
-
-$`Standard Error`
-[1] 0.02215619
-
-$Confidence
-[1] 0.07434261
-
-$`Confidence Limits`
-[1] 0.03113656 0.17982178
-
-> lod <- lod(m, alpha = 0.01, beta = 0.5)
-> lod(m, alpha = 0.01, beta = 0.01) 
-$x
-[1] 0.132904
-
-$y
-[1] 3764.977
-
-> loq <- loq(m, alpha = 0.01)
-> 
diff --git a/tests/massart97.R b/tests/massart97.R
deleted file mode 100644
index 00f837f..0000000
--- a/tests/massart97.R
+++ /dev/null
@@ -1,25 +0,0 @@
-require(chemCal)
-data(massart97ex1)
-m <- lm(y ~ x, data = massart97ex1)
-inverse.predict(m, 15)        #  6.1 +- 4.9
-inverse.predict(m, 90)        # 43.9 +- 4.9
-inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-
-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)
-weights <- w[factor(x)]
-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
deleted file mode 100644
index cb113d0..0000000
--- a/tests/massart97.Rout.save
+++ /dev/null
@@ -1,123 +0,0 @@
-
-R : Copyright 2006, The R Foundation for Statistical Computing
-Version 2.3.1 (2006-06-01)
-ISBN 3-900051-07-0
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-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.
-
-> require(chemCal)
-Loading required package: chemCal
-[1] TRUE
-> data(massart97ex1)
-> m <- lm(y ~ x, data = massart97ex1)
-> inverse.predict(m, 15)        #  6.1 +- 4.9
-$Prediction
-[1] 6.09381
-
-$`Standard Error`
-[1] 1.767278
-
-$Confidence
-[1] 4.906751
-
-$`Confidence Limits`
-[1]  1.187059 11.000561
-
-> inverse.predict(m, 90)        # 43.9 +- 4.9
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.767747
-
-$Confidence
-[1] 4.908053
-
-$`Confidence Limits`
-[1] 39.03178 48.84788
-
-> inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.141204
-
-$Confidence
-[1] 3.168489
-
-$`Confidence Limits`
-[1] 40.77134 47.10832
-
-> 
-> 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)
-> weights <- w[factor(x)]
-> m <- lm(y ~ x, w = weights)
-> #calplot(m)
-> 
-> inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
-$Prediction
-[1] 5.865367
-
-$`Standard Error`
-[1] 0.892611
-
-$Confidence
-[1] 2.478285
-
-$`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
-
-> 
-> 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
-
->