plotDS function

Plot Data and Smoother / Fitted Values

For one-dimensional nonparametric regression, plot the data and fitted values, typically a smooth function, and optionally use segments to visualize the residuals.

plotDS(x, yd, ys, xlab = "", ylab = "", ylim = rrange(c(yd, ys)),
       xpd = TRUE, do.seg = TRUE, seg.p = 0.95,
       segP = list(lty = 2, lwd = 1,   col = 2),
       linP = list(lty = 1, lwd = 2.5, col = 3),
       ...)

Arguments

• x, yd, ys: numeric vectors all of the same length, representing $(x_i, y_i)$ and fitted (smooth) values y^_i. x will be sorted increasingly if necessary, and yd and ys accordingly.

  Alternatively, ys can be an x-y list (as resulting from xy.coords) containing fitted values on a finer grid than the observations x. In that case, the observational values x[] must be part of the larger set; seqXtend() may be applied to construct such a set of abscissa values.

• xlab, ylab: x- and y- axis labels, as in plot.default.

• ylim: limits of y-axis to be used; defaults to a robust

  range of the values.

• xpd: see par(xpd=.); by default do allow to draw outside the plot region.

• do.seg: logical indicating if residual segments should be drawn, at x[i], from yd[i] to ys[i] (approximately, see seg.p).

• seg.p: segment percentage of segments to be drawn, from yd to seg.p*ys + (1-seg.p)*yd.

• segP: list with named components lty, lwd, col specifying line type, width and color for the residual segments, used only when do.seg is true.

• linP: list with named components lty, lwd, col specifying line type, width and color for smooth curve lines .

• ...: further arguments passed to plot.

Author(s)

Martin Maechler, since 1990

Note

Non-existing components in the lists segP or linP

will result in the par defaults to be used.

plotDS() used to be called pl.ds up to November 2007.

See Also

seqXtend() to construct more smooth ys

objects .

Examples

data(cars)
 x <-  cars$speed
 yd <- cars$dist
 ys <- lowess(x, yd, f = .3)$y
 plotDS(x, yd, ys)

 ## More interesting : Version of example(Theoph)
 data(Theoph)
 Th4 <- subset(Theoph, Subject == 4)
 ## just for "checking" purposes -- permute the observations:
 Th4 <- Th4[sample(nrow(Th4)), ]
 fm1 <- nls(conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data = Th4)

 ## Simple
 plotDS(Th4$Time, Th4$conc, fitted(fm1),
        sub  = "Theophylline data - Subject 4 only",
        segP = list(lty=1,col=2), las = 1)

 ## Nicer: Draw the smoother not only at x = x[i] (observations):
 xsm <- unique(sort(c(Th4$Time, seq(0, 25, length = 201))))
 ysm <- c(predict(fm1, newdata = list(Time = xsm)))
 plotDS(Th4$Time, Th4$conc, ys = list(x=xsm, y=ysm),
        sub  = "Theophylline data - Subject 4 only",
        segP = list(lwd=2), las = 1)