- Took loq and lod apart again. lod is now an implemantation of Massart, loq is

  an own variant of DIN 32645 (relative error on x axis).
- Partly make functions work on models where x and y are named different
  from "x" and "y" (loq to be done).




git-svn-id: http://kriemhild.uft.uni-bremen.de/svn/chemCal@11 5fad18fb-23f0-0310-ab10-e59a3bee62b4

4 files changed, 35 insertions, 23 deletions

diff --git a/R/calplot.R b/R/calplot.R
index feb9727..2deed5a 100644
--- a/R/calplot.R
+++ b/R/calplot.R
@@ -21,9 +21,10 @@ calplot.lm <- function(object, xlim = "auto", ylim = "auto",
 
   m <- object
   level <- 1 - alpha
-  x <- m$model$x
-  y <- m$model$y
-  newdata <- data.frame(x = seq(0,max(x),length=250))
+  y <- m$model[[1]]
+  x <- m$model[[2]]
+  newdata <- list(x = seq(0,max(x),length=250))
+  names(newdata) <- names(m$model)[[2]]
   pred.lim <- predict(m, newdata, interval = "prediction",level=level)
   conf.lim <- predict(m, newdata, interval = "confidence",level=level)
   if (xlim == "auto") xlim = c(0,max(x))
@@ -36,9 +37,9 @@ calplot.lm <- function(object, xlim = "auto", ylim = "auto",
     ylim = ylim
   )
   points(x,y, pch = 21, bg = "yellow")
-  matlines(newdata$x, pred.lim, lty = c(1, 4, 4), 
+  matlines(newdata[[1]], pred.lim, lty = c(1, 4, 4), 
     col = c("black", "red", "red"))
-  matlines(newdata$x, conf.lim, lty = c(1, 3, 3), 
+  matlines(newdata[[1]], conf.lim, lty = c(1, 3, 3), 
     col = c("black", "green4", "green4"))
 
   legend(min(x), 
diff --git a/R/inverse.predict.lm.R b/R/inverse.predict.lm.R
index 2ead9fb..e5f014c 100644
--- a/R/inverse.predict.lm.R
+++ b/R/inverse.predict.lm.R
@@ -17,32 +17,36 @@ inverse.predict.default <- function(object, newdata, ...,
 inverse.predict.lm <- function(object, newdata, ...,
   ws = "auto", alpha = 0.05, ss = "auto")
 {
+  yname = names(object$model)[[1]]
+  xname = names(object$model)[[2]]
   if (ws == "auto") {
     ws <- ifelse(length(object$weights) > 0, mean(object$weights), 1)
   }
   if (length(object$weights) > 0) {
-    wx <- split(object$weights,object$model$x)
+    wx <- split(object$weights,object$model[[xname]])
     w <- sapply(wx,mean)
   } else {
-    w <- rep(1,length(split(object$model$y,object$model$x)))
+    w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
   }
   .inverse.predict(object = object, newdata = newdata, 
-    ws = ws, alpha = alpha, ss = ss, w = w)
+    ws = ws, alpha = alpha, ss = ss, w = w, xname = xname, yname = yname)
 }
 
 inverse.predict.rlm <- function(object, newdata, ...,
   ws = "auto", alpha = 0.05, ss = "auto")
 {
+  yname = names(object$model)[[1]]
+  xname = names(object$model)[[2]]
   if (ws == "auto") {
     ws <- mean(object$w)
   }
-  wx <- split(object$weights,object$model$x)
+  wx <- split(object$weights,object$model[[xname]])
   w <- sapply(wx,mean)
   .inverse.predict(object = object, newdata = newdata, 
-    ws = ws, alpha = alpha, ss = ss, w = w)
+    ws = ws, alpha = alpha, ss = ss, w = w, xname = xname, yname = yname)
 }
 
-.inverse.predict <- function(object, newdata, ws, alpha, ss, w)
+.inverse.predict <- function(object, newdata, ws, alpha, ss, w, xname, yname)
 {
   if (length(object$coef) > 2)
     stop("More than one independent variable in your model - not implemented")
@@ -53,12 +57,12 @@ inverse.predict.rlm <- function(object, newdata, ...,
   ybars <- mean(newdata)
   m <- length(newdata)
 
-  yx <- split(object$model$y,object$model$x)
+  yx <- split(object$model[[yname]],object$model[[xname]])
   n <- length(yx)
   df <- n - length(objects$coef)
   x <- as.numeric(names(yx))
   ybar <- sapply(yx,mean)
-  yhatx <- split(object$fitted.values,object$model$x)
+  yhatx <- split(object$fitted.values,object$model[[xname]])
   yhat <- sapply(yhatx,mean)
   se <- sqrt(sum(w*(ybar - yhat)^2)/df)
   if (ss == "auto") {
@@ -67,7 +71,7 @@ inverse.predict.rlm <- function(object, newdata, ...,
     ss <- ss
   }
 
-  b1 <- object$coef[["x"]]
+  b1 <- object$coef[[xname]]
 
   ybarw <- sum(w * ybar)/sum(w)
 
diff --git a/R/lod.R b/R/lod.R
index 1bb7981..73f5353 100644
--- a/R/lod.R
+++ b/R/lod.R
@@ -10,18 +10,25 @@ lod.default <- function(object, ..., alpha = 0.05, beta = 0.05)
 
 lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05)
 {
-  y0 <- predict(object, data.frame(x = 0), interval="prediction", 
-      level = 1 - alpha)
+  xname <- names(object$model)[[2]]
+  newdata <- data.frame(0)
+  names(newdata) <- xname
+  y0 <- predict(object, newdata, interval="prediction", 
+      level = 1 - 2 * alpha )
   yc <- y0[[1,"upr"]]
   xc <- inverse.predict(object,yc)[["Prediction"]]
   f <- function(x)
   {
-      # Here I need the variance of y values as a function of x or
-      # y values
-      # Strangely, setting the confidence level to 0.5 does not result
-      # in a zero confidence or prediction interval
+    newdata <- data.frame(x)
+    names(newdata) <- xname
+    pi.y <- predict(object, newdata, interval = "prediction",
+      level = 1 - 2 * beta)
+    yd <- pi.y[[1,"lwr"]]
+    (yd - yc)^2
   }
-  lod.x <- optimize(f,interval=c(0,max(object$model$x)))$minimum
-  lod.y <-  predict(object, data.frame(x = lod.x))
+  lod.x <- optimize(f,interval=c(0,max(object$model[[xname]])))$minimum
+  newdata <- data.frame(x = lod.x)
+  names(lod.x) <- xname
+  lod.y <-  predict(object, data.frame(lod.x))
   return(list(x = lod.x, y = lod.y))
 }
diff --git a/R/loq.R b/R/loq.R
index 33e9556..c493a64 100644
--- a/R/loq.R
+++ b/R/loq.R
@@ -5,7 +5,7 @@ loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
 
 loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
 {
-  stop("lod is only implemented for univariate lm objects.")
+  stop("loq is only implemented for univariate lm objects.")
 }
 
 loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")