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#' Plot calibration graphs from univariate linear models
#'
#' Produce graphics of calibration data, the fitted model as well as
#' confidence, and, for unweighted regression, prediction bands.
#'
#' @aliases calplot calplot.default calplot.lm
#' @param object A univariate model object of class \code{\link{lm}} or
#' \code{\link[MASS:rlm]{rlm}} with model formula \code{y ~ x} or \code{y ~ x -
#' 1}.
#' @param xlim The limits of the plot on the x axis.
#' @param ylim The limits of the plot on the y axis.
#' @param xlab The label of the x axis.
#' @param ylab The label of the y axis.
#' @param legend_x An optional numeric value for adjusting the x coordinate of
#' the legend.
#' @param alpha The error tolerance level for the confidence and prediction
#' bands. Note that this includes both tails of the Gaussian distribution,
#' unlike the alpha and beta parameters used in \code{\link{lod}} (see note
#' below).
#' @param varfunc The variance function for generating the weights in the
#' model. Currently, this argument is ignored (see note below).
#' @return A plot of the calibration data, of your fitted model as well as
#' lines showing the confidence limits. Prediction limits are only shown for
#' models from unweighted regression.
#' @note Prediction bands for models from weighted linear regression require
#' weights for the data, for which responses should be predicted. Prediction
#' intervals using weights e.g. from a variance function are currently not
#' supported by the internally used function \code{\link{predict.lm}},
#' therefore, \code{calplot} does not draw prediction bands for such models.
#'
#' It is possible to compare the \code{\link{calplot}} prediction bands with
#' the \code{\link{lod}} values if the \code{lod()} alpha and beta parameters
#' are half the value of the \code{calplot()} alpha parameter.
#' @author Johannes Ranke
#' @importFrom graphics legend matlines plot points
#' @examples
#'
#' data(massart97ex3)
#' m <- lm(y ~ x, data = massart97ex3)
#' calplot(m)
#'
#' @export calplot
calplot <- function(object,
xlim = c("auto", "auto"), ylim = c("auto", "auto"),
xlab = "Concentration", ylab = "Response",
legend_x = "auto",
alpha = 0.05, varfunc = NULL)
{
UseMethod("calplot")
}
#' @export
calplot.default <- function(object,
xlim = c("auto","auto"), ylim = c("auto","auto"),
xlab = "Concentration", ylab = "Response",
legend_x = "auto",
alpha=0.05, varfunc = NULL)
{
stop("Calibration plots only implemented for univariate lm objects.")
}
#' @export
calplot.lm <- function(object,
xlim = c("auto","auto"), ylim = c("auto","auto"),
xlab = "Concentration", ylab = "Response",
legend_x = "auto",
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]
if (legend_x[1] == "auto") legend_x <- min(object$model[[2]])
plot(1,
type = "n",
xlab = xlab,
ylab = ylab,
xlim = as.numeric(xlim),
ylim = as.numeric(ylim)
)
points(x,y, pch = 21, bg = "yellow")
matlines(newdata[[1]], pred.lim, lty = c(1, 4, 4),
col = c("black", "red", "red"))
if (length(object$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(legend_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")
}
}
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