1 files changed, 0 insertions, 115 deletions
diff --git a/trunk/chemCal/R/inverse.predict.lm.R b/trunk/chemCal/R/inverse.predict.lm.R
deleted file mode 100644
index d57275c..0000000
--- a/trunk/chemCal/R/inverse.predict.lm.R
+++ /dev/null
@@ -1,115 +0,0 @@
-# This is an implementation of Equation (8.28) in the Handbook of Chemometrics
-# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
-# Example 8 on the same page, extended as specified in the package vignette
-
-inverse.predict <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  UseMethod("inverse.predict")
-}
-
-inverse.predict.default <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  stop("Inverse prediction only implemented for univariate lm and nls objects.")
-}
-
-inverse.predict.lm <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "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$model[[xname]])
-    w <- sapply(wx,mean)
-  } else {
-    w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
-  }
-  .inverse.predict(object = object, newdata = newdata, 
-    ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
-}
-
-inverse.predict.rlm <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "auto")
-{
-  yname = names(object$model)[[1]]
-  xname = names(object$model)[[2]]
-  if (ws == "auto") {
-    ws <- mean(object$w)
-  }
-  wx <- split(object$weights,object$model[[xname]])
-  w <- sapply(wx,mean)
-  .inverse.predict(object = object, newdata = newdata, 
-    ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
-}
-
-inverse.predict.nls <- function(object, newdata, ...,
-  ws = "auto", alpha = 0.05, var.s = "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$model[[xname]])
-    w <- sapply(wx,mean)
-  } else {
-    w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
-  }
-  if (length(object$coef) > 2)
-    stop("More than one independent variable in your model - not implemented")
-}
-
-.inverse.predict <- function(object, newdata, ws, alpha, var.s, w, xname, yname)
-{
-  if (length(object$coef) > 2)
-    stop("More than one independent variable in your model - not implemented")
-
-  if (alpha <= 0 | alpha >= 1)
-    stop("Alpha should be between 0 and 1 (exclusive)")
-
-  ybars <- mean(newdata)
-  m <- length(newdata)
-
-  yx <- split(object$model[[yname]], object$model[[xname]])
-  n <- length(yx)
-  df <- n - length(object$coef)
-  x <- as.numeric(names(yx))
-  ybar <- sapply(yx,mean)
-  yhatx <- split(object$fitted.values, object$model[[xname]])
-  yhat <- sapply(yhatx, mean)
-  se <- sqrt(sum(w * (ybar - yhat)^2)/df)
-
-  if (var.s == "auto") {
-    var.s <- se^2/ws
-  }
-
-  b1 <- object$coef[[xname]]
-
-  ybarw <- sum(w * ybar)/sum(w)
-
-# This is the adapted form of equation 8.28 (see package vignette)
-  sxhats <- 1/b1 * sqrt(
-    (var.s / m) + 
-    se^2 * (1/sum(w) + 
-      (ybars - ybarw)^2 * sum(w) /
-        (b1^2 * (sum(w) * sum(w * x^2) - sum(w * x)^2)))
-  )
-
-  if (names(object$coef)[1] == "(Intercept)") {
-    b0 <- object$coef[["(Intercept)"]]
-  } else {
-    b0 <- 0
-  }
-
-  xs <- (ybars - b0) / b1
-  t <- qt(1-0.5*alpha, n - 2)
-  conf <- t * sxhats
-  result <- list("Prediction"=xs,"Standard Error"=sxhats,
-    "Confidence"=conf, "Confidence Limits"=c(xs - conf, xs + conf))
-  return(result)
-}