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)
}
