From 9e0dae397df8c18e7333d2604cae96b2a7927420 Mon Sep 17 00:00:00 2001
From: ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>
Date: Fri, 23 Jun 2006 15:33:27 +0000
Subject: - 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
---
 R/lod.R | 40 +++++++++++++++++++++++-----------------
 1 file changed, 23 insertions(+), 17 deletions(-)

(limited to 'R/lod.R')

diff --git a/R/lod.R b/R/lod.R
index 54618c8..f5bbb7d 100644
--- a/R/lod.R
+++ b/R/lod.R
@@ -1,14 +1,14 @@
-lod <- function(object, ..., alpha = 0.05, beta = 0.05)
+lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
 {
   UseMethod("lod")
 }
 
-lod.default <- function(object, ..., alpha = 0.05, beta = 0.05)
+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)
+lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
 {
   if (length(object$weights) > 0) {
     stop(paste(
@@ -24,23 +24,29 @@ lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05)
   yname <- names(object$model)[[1]]
   newdata <- data.frame(0)
   names(newdata) <- xname
-  y0 <- predict(object, newdata, interval="prediction", 
-      level = 1 - 2 * alpha )
+  y0 <- predict(object, newdata, interval = "prediction", 
+    level = 1 - 2 * alpha)
   yc <- y0[[1,"upr"]]
-  xc <- inverse.predict(object,yc)[["Prediction"]]
-  f <- function(x)
-  {
-    newdata <- data.frame(x)
-    names(newdata) <- xname
-    pi.y <- predict(object, newdata, interval = "prediction",
+  if (method == "din") {
+    y0.d <- predict(object, newdata, interval = "prediction",
       level = 1 - 2 * beta)
-    yd <- pi.y[[1,"lwr"]]
-    (yd - yc)^2
+    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]])))$minimum
+    newdata <- data.frame(x = lod.x)
+    names(newdata) <- xname
+    lod.y <-  predict(object, newdata)
   }
-  lod.x <- optimize(f,interval=c(0,max(object$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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