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-rw-r--r--R/calplot.R80
-rw-r--r--R/inverse.predict.lm.R97
-rw-r--r--R/lod.R55
-rw-r--r--R/loq.R41
4 files changed, 273 insertions, 0 deletions
diff --git a/R/calplot.R b/R/calplot.R
new file mode 100644
index 0000000..6aed9c0
--- /dev/null
+++ b/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$weights) > 0) {
+ legend(min(x),
+ max(pred.lim, na.rm = TRUE),
+ legend = c("Fitted Line", "Confidence Bands"),
+ lty = c(1, 3),
+ lwd = 2,
+ col = c("black", "green4"),
+ horiz = FALSE, cex = 0.9, bg = "gray95")
+ } else {
+ matlines(newdata[[1]], conf.lim, lty = c(1, 3, 3),
+ col = c("black", "green4", "green4"))
+ legend(min(x),
+ max(pred.lim, na.rm = TRUE),
+ legend = c("Fitted Line", "Confidence Bands",
+ "Prediction Bands"),
+ lty = c(1, 3, 4),
+ lwd = 2,
+ col = c("black", "green4", "red"),
+ horiz = FALSE, cex = 0.9, bg = "gray95")
+ }
+}
diff --git a/R/inverse.predict.lm.R b/R/inverse.predict.lm.R
new file mode 100644
index 0000000..927e672
--- /dev/null
+++ b/R/inverse.predict.lm.R
@@ -0,0 +1,97 @@
+# This is an implementation of Equation (8.28) in the Handbook of Chemometrics
+# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
+# Example 8 on the same page, extended as specified in the package vignette
+
+inverse.predict <- function(object, newdata, ...,
+ ws = "auto", alpha = 0.05, var.s = "auto")
+{
+ UseMethod("inverse.predict")
+}
+
+inverse.predict.default <- function(object, newdata, ...,
+ ws = "auto", alpha = 0.05, var.s = "auto")
+{
+ stop("Inverse prediction only implemented for univariate lm objects.")
+}
+
+inverse.predict.lm <- function(object, newdata, ...,
+ ws = "auto", alpha = 0.05, var.s = "auto")
+{
+ yname = names(object$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$model[[xname]])
+ w <- sapply(wx,mean)
+ .inverse.predict(object = object, newdata = newdata,
+ ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
+}
+
+.inverse.predict <- function(object, newdata, ws, alpha, var.s, w, xname, yname)
+{
+ if (length(object$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/R/lod.R b/R/lod.R
new file mode 100644
index 0000000..5b74418
--- /dev/null
+++ b/R/lod.R
@@ -0,0 +1,55 @@
+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)
+}
diff --git a/R/loq.R b/R/loq.R
new file mode 100644
index 0000000..f832265
--- /dev/null
+++ b/R/loq.R
@@ -0,0 +1,41 @@
+loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+ var.loq = "auto", tol = "default")
+{
+ UseMethod("loq")
+}
+
+loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+ var.loq = "auto", tol = "default")
+{
+ 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", tol = "default")
+{
+ 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]]
+ xvalues <- 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
+ }
+ if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
+ loq.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$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)
+}

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