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-rw-r--r--R/lod.R40
1 files changed, 23 insertions, 17 deletions
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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