R/rg-boxplot.R

"rg.boxplot" <- 
function(xx, xlab = deparse(substitute(xx)), log = FALSE, ifbw = FALSE, wend = 0.050000000000000003,
	xlim = NULL, main = " ", colr = 5, ...)
{
	# Function to plot a single horizontal box plot; the default is a Tukey boxplot, 
	# ifbw = T generates an IDEAS style box-and-whisker plot, and  wend defines the
	# the end of the whisker - default is 5th and 95th %ile ; setting log = T generates
	# log-scaled plots and a log transformation of the data for the Tukey boxplot
	# outlier calculations.  The box is infilled with a yellow ochre, colr = 5; if no colour
	# is required, set colr = 0.  Settng xlim results in outliers not being plotted as the x-
	# axis is shortened, however, the statistics used to define the boxplot, or box-
	# and-whisker plot, are still based on the total data set.  To plot a truncated data
	# set create a subset first , or use the x[x<some.value] construct in the call.
	# Additional graphical parameters can be defined in "..."
###	frame()
	temp.x <- rg.remove.na(xx)
	x <- temp.x$x
	nx <- length(x)
	rangex <- range(x)
	if(log) {
		logplot <- "x"
		if((!is.null(xlim)) && (xlim[1] <= 0))
			xlim[1] <- rangex[1]
	}
	else logplot <- ""
	if(is.null(xlim)) {
		plot(x = rangex, y = c(0, 0), xlab = xlab, ylab = "", log = logplot, ylim = c(
			-1, 1), yaxt = "n", type = "n", main = main, ...)
		limits <- par("usr")
		nxx <- 0
	}
	else {
		plot(x = rangex, y = c(0, 0), xlab = xlab, ylab = "", log = logplot, xlim = xlim,
			ylim = c(-1, 1), yaxt = "n", type = "n", main = main, ...)
		limits <- par("usr")
		if(log)
			limits[1:2] <- 10^limits[1:2]
		dropped <- x[(x > xlim[2]) | (x < xlim[1])]
		nxx <- length(dropped)
	}
	if(ifbw) {
		if(wend <= 9.9999999999999995e-008) {
			lowend <- rangex[1]
			hihend <- rangex[2]
			q <- as.vector(quantile(x, probs = c(0.25, 0.5, 0.75), na.rm = TRUE))
			q1 <- q[1]
			q2 <- q[2]
			q3 <- q[3]
		}
		else {
			points(rangex, c(0, 0), pch = 3)
			if(wend > 0.25)
				wend <- 0.050000000000000003
			q <- as.vector(quantile(x, probs = c(wend, 0.25, 0.5, 0.75, 1 - wend),
				na.rm = TRUE))
			lowend <- q[1]
			q1 <- q[2]
			q2 <- q[3]
			q3 <- q[4]
			hihend <- q[5]
		}
	}
	else {
		q <- as.vector(quantile(x, probs = c(0.25, 0.5, 0.75), na.rm = TRUE))
		xcut <- numeric(4)
		if(log) {
			lq1 <- log10(q[1])
			lq3 <- log10(q[3])
			hw <- lq3 - lq1
			xcut[1] <- 10^(lq1 - 3 * hw)
			xcut[2] <- 10^(lq1 - 1.5 * hw)
			xcut[3] <- 10^(lq3 + 1.5 * hw)
			xcut[4] <- 10^(lq3 + 3 * hw)
		}
		else {
			hw <- q[3] - q[1]
			xcut[1] <- q[1] - 3 * hw
			xcut[2] <- q[1] - 1.5 * hw
			xcut[3] <- q[3] + 1.5 * hw
			xcut[4] <- q[3] + 3 * hw
		}
		lowend <- min(x[x > xcut[2]])
		q1 <- q[1]
		q2 <- q[2]
		q3 <- q[3]
		hihend <- max(x[x < xcut[3]])
		for(i in 1:nx) {
			if((x[i] < lowend) || (x[i] > hihend)) {
				pch <- 3
				if((x[i] <= xcut[1]) || (x[i] >= xcut[4]))
					pch <- 1
				if(is.null(xlim) || ((x[i] <= xlim[2]) & (x[i] >= xlim[1]))) {
					points(x[i], 0, pch = pch)
				}
			}
		}
	}
	polygon(c(q1, q1, q3, q3, q1), c(0.40000000000000002, -0.40000000000000002, 
		-0.40000000000000002, 0.40000000000000002, 0.40000000000000002), col = colr)
	ypos <- c(0, 0, 0.40000000000000002, 0.40000000000000002, 0, 0, 0, -0.40000000000000002,
		-0.40000000000000002, 0.40000000000000002, -0.40000000000000002, 
		-0.40000000000000002, 0)
	xpos <- c(lowend, q1, q1, q3, q3, hihend, q3, q3, q2, q2, q2, q1, q1)
	lines(xpos, ypos)
	if(log)
		xpos <- 10^(log10(limits[2]) - (log10(limits[2]) - log10(limits[1])) * 
			0.050000000000000003)
	else xpos <- limits[2] - (limits[2] - limits[1]) * 0.050000000000000003
	ypos <- limits[3] + (limits[4] - limits[3]) * 0.11
	text(xpos, ypos, labels = paste("n =", nx), adj = 1)
	if(nxx != 0) {
		ypos <- limits[3] + (limits[4] - limits[3]) * 0.050000000000000003
		text(xpos, ypos, labels = paste(nxx, "points not plotted"), adj = 1)
	}
	invisible()
}

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StatDA documentation built on March 13, 2020, 2:42 a.m.