Get rid of the branched svn layout I never used

git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@36 5fad18fb-23f0-0310-ab10-e59a3bee62b4

1 files changed, 55 insertions, 0 deletions

diff --git a/R/lod.R b/R/lod.R
new file mode 100644
index 0000000..5b74418
--- /dev/null
+++ b/R/lod.R
@@ -0,0 +1,55 @@
+lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
+{
+  UseMethod("lod")
+}
+
+lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
+{
+  stop("lod is only implemented for univariate lm objects.")
+}
+
+lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "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]]
+  xvalues <- 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)$Prediction
+  } else {
+    f <- function(x) {
+      newdata <- data.frame(x)
+      names(newdata) <- xname
+      pi.y <- predict(object, newdata, interval = "prediction",
+        level = 1 - 2 * beta)
+      yd <- pi.y[[1,"lwr"]]
+      (yd - yc)^2
+    }
+    if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
+    lod.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$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)
+}