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$coef) > 2) stop("More than one independent variable in your model - not implemented") if (length(object$weights) > 0) { stop(paste( "\nConfidence and prediction intervals for weighted linear models require", "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")) } 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")) 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") }