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authorranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2015-08-22 09:03:10 +0000
committerranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>2015-08-22 09:03:10 +0000
commitd8d6012e98fb4c7f158bcc7c173407c2b5f3e42e (patch)
tree92bcbbc548431b214fb387e20dc423745b2ab897 /R/lod.R
parent2be973ef45816e04a6a59f59a4fae50f8f17a5e1 (diff)
Get rid of the branched svn layout I never used
git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@36 5fad18fb-23f0-0310-ab10-e59a3bee62b4
Diffstat (limited to 'R/lod.R')
-rw-r--r--R/lod.R55
1 files changed, 55 insertions, 0 deletions
diff --git a/R/lod.R b/R/lod.R
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--- /dev/null
+++ b/R/lod.R
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+lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
+{
+ UseMethod("lod")
+}
+
+lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "default")
+{
+ stop("lod is only implemented for univariate lm objects.")
+}
+
+lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default", tol = "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]]
+ xvalues <- 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)$Prediction
+ } else {
+ f <- function(x) {
+ newdata <- data.frame(x)
+ names(newdata) <- xname
+ pi.y <- predict(object, newdata, interval = "prediction",
+ level = 1 - 2 * beta)
+ yd <- pi.y[[1,"lwr"]]
+ (yd - yc)^2
+ }
+ if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
+ lod.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$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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