- 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

1 files changed, 6 insertions, 6 deletions

diff --git a/man/calplot.lm.Rd b/man/calplot.lm.Rd
index de63022..bf3f616 100644
--- a/man/calplot.lm.Rd
+++ b/man/calplot.lm.Rd
@@ -5,12 +5,11 @@
 \title{Plot calibration graphs from univariate linear models}
 \description{
 	Produce graphics of calibration data, the fitted model as well
-  as prediction and confidence bands. 
+  as confidence, and, for unweighted regression, prediction bands. 
 }
 \usage{
-  calplot(object, xlim = c("auto","auto"), ylim = c("auto","auto"),
-  xlab = "Concentration", ylab = "Response", alpha=0.05, 
-  varfunc = NULL)
+  calplot(object, xlim = c("auto", "auto"), ylim = c("auto", "auto"),
+    xlab = "Concentration", ylab = "Response", alpha=0.05, varfunc = NULL)
 }
 \arguments{
   \item{object}{
@@ -40,7 +39,8 @@
 }
 \value{
   A plot of the calibration data, of your fitted model as well as lines showing
-  the confidence limits as well as the prediction limits.
+  the confidence limits. Prediction limits are only shown for models from
+  unweighted regression.
 } 
 \note{
   Prediction bands for models from weighted linear regression require weights
@@ -51,7 +51,7 @@
 }
 \examples{
 data(massart97ex3)
-m <- lm(y ~ x, data=massart97ex3)
+m <- lm(y ~ x, data = massart97ex3)
 calplot(m)
 }
 \author{