sfsmisc: plotDS – R documentation

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plotDS

Plot Data and Smoother / Fitted Values

Description

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

Usage

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.pys + (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`.

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.

Author(s)

Martin Maechler, since 1990

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)

sfsmisc

Utilities from 'Seminar fuer Statistik' ETH Zurich

v1.1-11

GPL (>= 2)

Authors

Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Werner Stahel [ctb] (Functions: compresid2way(), f.robftest(), last(), p.scales(), p.dnorm()), Andreas Ruckstuhl [ctb] (Functions: p.arrows(), p.profileTraces(), p.res.2x()), Christian Keller [ctb] (Functions: histBxp(), p.tachoPlot()), Kjetil Halvorsen [ctb] (Functions: KSd(), ecdf.ksCI()), Alain Hauser [ctb] (Functions: cairoSwd(), is.whole(), toLatex.numeric()*), Christoph Buser [ctb] (to function Duplicated()), Lorenz Gygax [ctb] (to function p.res.2fact()), Bill Venables [ctb] (Functions: empty.dimnames(), primes()), Tony Plate [ctb] (to inv.seq()), Isabelle Fl<fc>ckiger [ctb], Marcel Wolbers [ctb], Markus Keller [ctb], Sandrine Dudoit [ctb], Jane Fridlyand [ctb], Greg Snow [ctb] (to loessDemo()), Henrik Aa. Nielsen [ctb] (to loessDemo()), Vincent Carey [ctb], Ben Bolker [ctb], Philippe Grosjean [ctb], Fr<e9>d<e9>ric Ibanez [ctb], Caterina Savi [ctb], Charles Geyer [ctb], Jens Oehlschl<e4>gel [ctb]

Initial release

2021-04-03