Don't do calplot and lod for linear models from weighted

regression any more, since this is not supported (PR#8877).



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@13 5fad18fb-23f0-0310-ab10-e59a3bee62b4

1 files changed, 13 insertions, 3 deletions

diff --git a/R/loq.R b/R/loq.R
index c493a64..ee22d38 100644
--- a/R/loq.R
+++ b/R/loq.R
@@ -10,11 +10,21 @@ loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
 
 loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
 {
+  xname <- names(object$model)[[2]]
+  yname <- names(object$model)[[1]]
   f <- function(x) {
-    y <- predict(object, data.frame(x = 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[["Prediction"]] - k * p[["Confidence"]])^2
   }
-  tmp <- optimize(f,interval=c(0,max(object$model$x)))
-  return(tmp$minimum)
+  tmp <- optimize(f,interval=c(0,max(object$model[[2]])))
+  loq.x <- tmp$minimum
+  newdata <- data.frame(x = loq.x)
+  names(newdata) <- xname
+  loq.y <- predict(object, newdata)
+  loq <- list(loq.x, loq.y)
+  names(loq) <- c(xname, yname)
+  return(loq)
 }