From 14a5af60a36071f6a9b4471fdf183fd91e89e1cd Mon Sep 17 00:00:00 2001
From: ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>
Date: Mon, 1 Oct 2007 19:44:04 +0000
Subject: Moved everything into the trunk directory, in order to enable
 branching

git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@22 5fad18fb-23f0-0310-ab10-e59a3bee62b4
---
 trunk/R/lod.R | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 trunk/R/lod.R

(limited to 'trunk/R/lod.R')

diff --git a/trunk/R/lod.R b/trunk/R/lod.R
new file mode 100644
index 0000000..f5bbb7d
--- /dev/null
+++ b/trunk/R/lod.R
@@ -0,0 +1,53 @@
+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)
+}
-- 
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