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-rw-r--r--R/lod.R53
1 files changed, 0 insertions, 53 deletions
diff --git a/R/lod.R b/R/lod.R
deleted file mode 100644
index f5bbb7d..0000000
--- a/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)
-}

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