pl.ds {sfsmisc} | R Documentation |
For one-dimensional nonparametric regression, plot the data and fitted values, typically a smooth function, and optionally use segments to visualize the residuals.
pl.ds(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), ...)
x, yd, ys |
numeric vectors all of the same length, representing
(x_i, y_i) and fitted (smooth) values y^_i.
Note that x will be sort increasingly if necessary, and
yd and ys accordingly. |
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 . |
Non-existing components in the lists segP
or linP
will result in the par
defaults to be used.
Martin Maechler, 1990-1994
data(cars) x <- cars$speed yd <- cars$dist ys <- lowess(x, yd,f = .2)$y pl.ds(x, yd, ys) ## More interesting : Version of example(Theoph) data(Theoph) Th4 <- subset(Theoph, Subject == 4) fm1 <- nls(conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data = Th4) pl.ds(Th4$Time, Th4$conc, fitted(fm1), sub = "Theophylline data - Subject 4 only", segP = list(lty=1,col=2), las = 1) xvals <- seq(0, par("usr")[2], len = 55) lines(xvals, predict(fm1, newdata = list(Time = xvals)), col = 4)