- 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, 14 insertions, 4 deletions

diff --git a/R/loq.R b/R/loq.R
index ee22d38..5776096 100644
--- a/R/loq.R
+++ b/R/loq.R
@@ -1,22 +1,32 @@
-loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
+loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto", 
+  var.loq = "auto")
 {
   UseMethod("loq")
 }
 
-loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
+loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+  var.loq = "auto")
 {
   stop("loq is only implemented for univariate lm objects.")
 }
 
-loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
+loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+  var.loq = "auto")
 {
+  if (length(object$weights) > 0 && var.loq == "auto" && w.loq == "auto") {
+    stop(paste("If you are using a model from weighted regression,",
+      "you need to specify a reasonable approximation for the",
+      "weight (w.loq) or the variance (var.loq) at the",
+      "limit of quantification"))
+  }
   xname <- names(object$model)[[2]]
   yname <- names(object$model)[[1]]
   f <- function(x) {
     newdata <- data.frame(x = x)
     names(newdata) <- xname
     y <- predict(object, newdata)
-    p <- inverse.predict(object, rep(y, n), ws = w, alpha = alpha)
+    p <- inverse.predict(object, rep(y, n), ws = w.loq, 
+        var.s = var.loq, alpha = alpha)
     (p[["Prediction"]] - k * p[["Confidence"]])^2
   }
   tmp <- optimize(f,interval=c(0,max(object$model[[2]])))