- Added ChangeLog

- Bugfix for lod() for the case of small x values (see ChangeLog)
- Version 0.1-32 as just submitted to CRAN
- Got rid of trunk directory, as I will not find the time to finish what I started there and it 
  may confuse visitors of viewcvs in kriemhild



git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@31 5fad18fb-23f0-0310-ab10-e59a3bee62b4

1 files changed, 0 insertions, 53 deletions

diff --git a/trunk/chemCal/R/lod.R b/trunk/chemCal/R/lod.R
deleted file mode 100644
index f5bbb7d..0000000
--- a/trunk/chemCal/R/lod.R
+++ /dev/null
@@ -1,53 +0,0 @@
-lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
-{
-  UseMethod("lod")
-}
-
-lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
-{
-  stop("lod is only implemented for univariate lm objects.")
-}
-
-lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
-{
-  if (length(object$weights) > 0) {
-    stop(paste(
-      "\nThe detemination of a lod from calibration models obtained by",
-      "weighted linear regression requires confidence intervals for",
-      "predicted y values taking into account weights for the x values",
-      "from which the predictions are to be generated.",
-      "This is not supported by the internally used predict.lm method.",
-      sep = "\n"
-    ))
-  }
-  xname <- names(object$model)[[2]]
-  yname <- names(object$model)[[1]]
-  newdata <- data.frame(0)
-  names(newdata) <- xname
-  y0 <- predict(object, newdata, interval = "prediction", 
-    level = 1 - 2 * alpha)
-  yc <- y0[[1,"upr"]]
-  if (method == "din") {
-    y0.d <- predict(object, newdata, interval = "prediction",
-      level = 1 - 2 * beta)
-    deltay <- y0.d[[1, "upr"]] - y0.d[[1, "fit"]]
-    lod.y <- yc + deltay 
-    lod.x <- inverse.predict(object, lod.y)$Prediction
-  } else {
-    f <- function(x) {
-      newdata <- data.frame(x)
-      names(newdata) <- xname
-      pi.y <- predict(object, newdata, interval = "prediction",
-        level = 1 - 2 * beta)
-      yd <- pi.y[[1,"lwr"]]
-      (yd - yc)^2
-    }
-    lod.x <- optimize(f,interval=c(0,max(object$model[[xname]])))$minimum
-    newdata <- data.frame(x = lod.x)
-    names(newdata) <- xname
-    lod.y <-  predict(object, newdata)
-  }
-  lod <- list(lod.x, lod.y)
-  names(lod) <- c(xname, yname)
-  return(lod)
-}