diff options
author | ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4> | 2006-06-23 15:33:27 +0000 |
---|---|---|
committer | ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4> | 2006-06-23 15:33:27 +0000 |
commit | 9e0dae397df8c18e7333d2604cae96b2a7927420 (patch) | |
tree | b513b791985426bab6c18850d2f8c308c411c1a5 /tests/din32645.Rout.save | |
parent | fb7ea47c774f67b8c26a6844f4ade8935a8653cc (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/din32645.Rout.save')
-rw-r--r-- | tests/din32645.Rout.save | 10 |
1 files changed, 6 insertions, 4 deletions
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 |