plot.heft: Heft: hazard estimation with flexible tails

In polspline: Polynomial Spline Routines

Heft: hazard estimation with flexible tails

Description

Plots a density, distribution function, hazard function or survival function for a heft object.

Usage

## S3 method for class 'heft'
plot(x, n = 100, what = "d", add = FALSE, xlim, xlab, ylab,
type, ...) 

Arguments

x	heft object, typically the result of heft.
n	the number of equally spaced points at which to plot the function.
what	what should be plotted: "d" (density), "p" (distribution function), "s" (survival function) or "h" (hazard function).
add	should the plot be added to an existing plot?
xlim	plotting limits; default is from the maximum of 0 and 10% before the 1st percentile to the minimmum of 10% further than the 99th percentile and the largest observation.
xlab,ylab	labels for the axes. The default is no labels.
type	plotting type. The default is lines.
...	all other plotting options are passed on.

Details

This function produces a plot of a heft 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.)

Author(s)

Charles Kooperberg [e-mail].

References

Charles Kooperberg, Charles J. Stone and Young K. Truong (1995). Hazard regression. Journal of the American Statistical Association, 90, 78-94.

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.

See Also

heft, summary.heft, dheft, hheft, pheft, qheft, rheft.

Examples

fit1 <- heft(testhare[,1], testhare[,2])
plot(fit1, what = "h")
# modify tail behavior
fit2 <- heft(testhare[,1], testhare[,2], leftlog = FALSE, rightlog = FALSE, 
    leftlin = TRUE)   
plot(fit2, what = "h", add = TRUE,lty = 2)
fit3 <- heft(testhare[,1], testhare[,2], penalty = 0)   # select largest model
plot(fit3, what = "h", add = TRUE,lty = 3)

polspline documentation built on Oct. 27, 2023, 1:07 a.m.