diff options
author | ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4> | 2015-08-22 09:03:10 +0000 |
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committer | ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4> | 2015-08-22 09:03:10 +0000 |
commit | d8d6012e98fb4c7f158bcc7c173407c2b5f3e42e (patch) | |
tree | 92bcbbc548431b214fb387e20dc423745b2ab897 /branches/0.1/chemCal/R/lod.R | |
parent | 2be973ef45816e04a6a59f59a4fae50f8f17a5e1 (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 'branches/0.1/chemCal/R/lod.R')
-rw-r--r-- | branches/0.1/chemCal/R/lod.R | 55 |
1 files changed, 0 insertions, 55 deletions
diff --git a/branches/0.1/chemCal/R/lod.R b/branches/0.1/chemCal/R/lod.R deleted file mode 100644 index 5b74418..0000000 --- a/branches/0.1/chemCal/R/lod.R +++ /dev/null @@ -1,55 +0,0 @@ -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) -} |