1 files changed, 41 insertions, 0 deletions
diff --git a/R/loq.R b/R/loq.R
new file mode 100644
index 0000000..f832265
--- /dev/null
+++ b/R/loq.R
@@ -0,0 +1,41 @@
+loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto", 
+  var.loq = "auto", tol = "default")
+{
+  UseMethod("loq")
+}
+
+loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+  var.loq = "auto", tol = "default")
+{
+  stop("loq is only implemented for univariate lm objects.")
+}
+
+loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+  var.loq = "auto", tol = "default")
+{
+  if (length(object$weights) > 0 && var.loq == "auto" && w.loq == "auto") {
+    stop(paste("If you are using a model from weighted regression,",
+      "you need to specify a reasonable approximation for the",
+      "weight (w.loq) or the variance (var.loq) at the",
+      "limit of quantification"))
+  }
+  xname <- names(object$model)[[2]]
+  xvalues <- object$model[[2]]
+  yname <- names(object$model)[[1]]
+  f <- function(x) {
+    newdata <- data.frame(x = x)
+    names(newdata) <- xname
+    y <- predict(object, newdata)
+    p <- inverse.predict(object, rep(y, n), ws = w.loq, 
+        var.s = var.loq, alpha = alpha)
+    (p[["Prediction"]] - k * p[["Confidence"]])^2
+  }
+  if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
+  loq.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$minimum
+  newdata <- data.frame(x = loq.x)
+  names(newdata) <- xname
+  loq.y <- predict(object, newdata)
+  loq <- list(loq.x, loq.y)
+  names(loq) <- c(xname, yname)
+  return(loq)
+}