Plots a logspline density, distribution function, hazard
function or survival function
from
a logspline density that was fitted using
the 1997 knot addition and deletion algorithm (logspline).
The 1992 algorithm is available using the oldlogspline function.
# S3 method for logspline
plot(x, n = 100, what = "d", add = FALSE, xlim, xlab = "",
ylab = "", type = "l", ...)logspline object, typically the result of logspline.
the number of equally spaced points at which to plot the density.
what should be plotted:
"d" (density), "p" (distribution function), "s" (survival
function) or "h" (hazard function).
should the plot be added to an existing plot.
range of data on which to plot. Default is from the 1th to the 99th percentile of the density, extended by 10% on each end.
labels plotted on the axes.
type of plot.
other plotting options, as desired
This function produces a plot of a logspline fit at n equally
spaced points roughly covering the support of the density. (Use
xlim = c(from, to) to change the range of these points.)
Charles Kooperberg and Charles J. Stone. Logspline density estimation for censored data (1992). Journal of Computational and Graphical Statistics, 1, 301--328.
Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371--1470.
logspline,
summary.logspline,
dlogspline,
plogspline,
qlogspline,
rlogspline,
# NOT RUN {
y <- rnorm(100)
fit <- logspline(y)
plot(fit)
# }
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