From 49eff36596275b1dbb5e07c97fb93db182baa27e Mon Sep 17 00:00:00 2001
From: ranke <ranke@5fad18fb-23f0-0310-ab10-e59a3bee62b4>
Date: Tue, 16 May 2006 19:49:08 +0000
Subject: - 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
---
 R/inverse.predict.lm.R | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

(limited to 'R/inverse.predict.lm.R')

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)
 
-- 
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