lod <- function(object, ..., alpha = 0.05, beta = 0.05)
{
  UseMethod("lod")
}

lod.default <- function(object, ..., alpha = 0.05, beta = 0.05)
{
  stop("lod is only implemented for univariate lm objects.")
}

lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05)
{
  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$model)[[1]]
  newdata <- data.frame(0)
  names(newdata) <- xname
  y0 <- predict(object, newdata, interval="prediction", 
      level = 1 - 2 * alpha )
  yc <- y0[[1,"upr"]]
  xc <- inverse.predict(object,yc)[["Prediction"]]
  f <- function(x)
  {
    newdata <- data.frame(x)
    names(newdata) <- xname
    pi.y <- predict(object, newdata, interval = "prediction",
      level = 1 - 2 * beta)
    yd <- pi.y[[1,"lwr"]]
    (yd - yc)^2
  }
  lod.x <- optimize(f,interval=c(0,max(object$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)
}