5 files changed, 290 insertions, 0 deletions
diff --git a/trunk/R/calfunctions.R b/trunk/R/calfunctions.R
new file mode 100644
index 0000000..6ce29f7
--- /dev/null
+++ b/trunk/R/calfunctions.R
@@ -0,0 +1,2 @@
+powfunc <- function(x,a,b) a * x^b
+ipowfunc <- function(y,a,b) (y/a)^1/b
diff --git a/trunk/R/calplot.R b/trunk/R/calplot.R
new file mode 100644
index 0000000..6aed9c0
--- /dev/null
+++ b/trunk/R/calplot.R
@@ -0,0 +1,80 @@
+calplot <- function(object, 
+  xlim = c("auto", "auto"), ylim = c("auto", "auto"), 
+  xlab = "Concentration", ylab = "Response", alpha = 0.05,
+  varfunc = NULL)
+{
+  UseMethod("calplot")
+}
+
+calplot.default <- function(object, 
+  xlim = c("auto","auto"), ylim = c("auto","auto"), 
+  xlab = "Concentration", ylab = "Response",
+  alpha=0.05, varfunc = NULL)
+{
+  stop("Calibration plots only implemented for univariate lm objects.")
+}
+
+calplot.lm <- function(object,
+  xlim = c("auto","auto"), ylim = c("auto","auto"), 
+  xlab = "Concentration", ylab = "Response", alpha=0.05,
+  varfunc = NULL)
+{
+  if (length(object$coef) > 2)
+    stop("More than one independent variable in your model - not implemented")
+
+  if (alpha <= 0 | alpha >= 1)
+    stop("Alpha should be between 0 and 1 (exclusive)")
+
+  m <- object
+  level <- 1 - alpha
+  y <- m$model[[1]]
+  x <- m$model[[2]]
+  if (xlim[1] == "auto") xlim[1] <- 0
+  if (xlim[2] == "auto") xlim[2] <- max(x)
+  xlim <- as.numeric(xlim)
+  newdata <- list(
+    x = seq(from = xlim[[1]], to = xlim[[2]], length=250))
+  names(newdata) <- names(m$model)[[2]]
+  if (is.null(varfunc)) {
+    varfunc <- if (length(m$weights)) {
+        function(variable) mean(m$weights)
+      } else function(variable) rep(1,250)
+  }
+  pred.lim <- predict(m, newdata, interval = "prediction",
+    level=level, weights.newdata = varfunc(m))
+  conf.lim <- predict(m, newdata, interval = "confidence",
+    level=level)
+  yrange.auto <- range(c(0,pred.lim))
+  if (ylim[1] == "auto") ylim[1] <- yrange.auto[1]
+  if (ylim[2] == "auto") ylim[2] <- yrange.auto[2]
+  plot(1,
+    type = "n", 
+    xlab = xlab,
+    ylab = ylab,
+    xlim = as.numeric(xlim),
+    ylim = as.numeric(ylim)
+  )
+  points(x,y, pch = 21, bg = "yellow")
+  matlines(newdata[[1]], pred.lim, lty = c(1, 4, 4), 
+    col = c("black", "red", "red"))
+  if (length(object$weights) > 0) {
+    legend(min(x), 
+      max(pred.lim, na.rm = TRUE), 
+      legend = c("Fitted Line", "Confidence Bands"),
+      lty = c(1, 3), 
+      lwd = 2, 
+      col = c("black", "green4"), 
+      horiz = FALSE, cex = 0.9, bg = "gray95")
+  } else {
+  matlines(newdata[[1]], conf.lim, lty = c(1, 3, 3), 
+    col = c("black", "green4", "green4"))
+  legend(min(x), 
+    max(pred.lim, na.rm = TRUE), 
+    legend = c("Fitted Line", "Confidence Bands", 
+        "Prediction Bands"), 
+    lty = c(1, 3, 4), 
+    lwd = 2, 
+    col = c("black", "green4", "red"), 
+    horiz = FALSE, cex = 0.9, bg = "gray95")
+  }
+}
diff --git a/trunk/R/inverse.predict.lm.R b/trunk/R/inverse.predict.lm.R
new file mode 100644
index 0000000..d57275c
--- /dev/null
+++ b/trunk/R/inverse.predict.lm.R
@@ -0,0 +1,115 @@
+# This is an implementation of Equation (8.28) in the Handbook of Chemometrics
+# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
+# Example 8 on the same page, extended as specified in the package vignette
+
+inverse.predict <- function(object, newdata, ...,
+  ws = "auto", alpha = 0.05, var.s = "auto")
+{
+  UseMethod("inverse.predict")
+}
+
+inverse.predict.default <- function(object, newdata, ...,
+  ws = "auto", alpha = 0.05, var.s = "auto")
+{
+  stop("Inverse prediction only implemented for univariate lm and nls objects.")
+}
+
+inverse.predict.lm <- function(object, newdata, ...,
+  ws = "auto", alpha = 0.05, var.s = "auto")
+{
+  yname = names(object$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)
+}
diff --git a/trunk/R/lod.R b/trunk/R/lod.R
new file mode 100644
index 0000000..f5bbb7d
--- /dev/null
+++ b/trunk/R/lod.R
@@ -0,0 +1,53 @@
+lod <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
+{
+  UseMethod("lod")
+}
+
+lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, method = "default")
+{
+  stop("lod is only implemented for univariate lm objects.")
+}
+
+lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, method = "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]]
+  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
+    }
+    lod.x <- optimize(f,interval=c(0,max(object$model[[xname]])))$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)
+}
diff --git a/trunk/R/loq.R b/trunk/R/loq.R
new file mode 100644
index 0000000..5776096
--- /dev/null
+++ b/trunk/R/loq.R
@@ -0,0 +1,40 @@
+loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto", 
+  var.loq = "auto")
+{
+  UseMethod("loq")
+}
+
+loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+  var.loq = "auto")
+{
+  stop("loq is only implemented for univariate lm objects.")
+}
+
+loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+  var.loq = "auto")
+{
+  if (length(object$weights) > 0 && var.loq == "auto" && w.loq == "auto") {
+    stop(paste("If you are using a model from weighted regression,",
+      "you need to specify a reasonable approximation for the",
+      "weight (w.loq) or the variance (var.loq) at the",
+      "limit of quantification"))
+  }
+  xname <- names(object$model)[[2]]
+  yname <- names(object$model)[[1]]
+  f <- function(x) {
+    newdata <- data.frame(x = x)
+    names(newdata) <- xname
+    y <- predict(object, newdata)
+    p <- inverse.predict(object, rep(y, n), ws = w.loq, 
+        var.s = var.loq, alpha = alpha)
+    (p[["Prediction"]] - k * p[["Confidence"]])^2
+  }
+  tmp <- optimize(f,interval=c(0,max(object$model[[2]])))
+  loq.x <- tmp$minimum
+  newdata <- data.frame(x = loq.x)
+  names(newdata) <- xname
+  loq.y <- predict(object, newdata)
+  loq <- list(loq.x, loq.y)
+  names(loq) <- c(xname, yname)
+  return(loq)
+}