lines.saddle.distn {boot} | R Documentation |
This function adds a line corresponding to a saddlepoint density or distribution function approximation to the current plot.
## S3 method for class 'saddle.distn' lines(x, dens = TRUE, h = function(u) u, J = function(u) 1, npts = 50, lty = 1, ...)
x |
An object of class |
dens |
A logical variable indicating whether the saddlepoint density
( |
h |
Any transformation of the variable that is required. Its first argument must be the value at which the approximation is being performed and the function must be vectorized. |
J |
When |
npts |
The number of points to be used for the plot. These points will be evenly spaced over the range of points used in finding the saddlepoint approximation. |
lty |
The line type to be used. |
... |
Any additional arguments to |
The function uses smooth.spline
to produce the saddlepoint
curve. When dens=TRUE
the spline is on the log scale and when
dens=FALSE
it is on the probit scale.
sad.d
is returned invisibly.
A line is added to the current plot.
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
# In this example we show how a plot such as that in Figure 9.9 of # Davison and Hinkley (1997) may be produced. Note the large number of # bootstrap replicates required in this example. expdata <- rexp(12) vfun <- function(d, i) { n <- length(d) (n-1)/n*var(d[i]) } exp.boot <- boot(expdata,vfun, R = 9999) exp.L <- (expdata - mean(expdata))^2 - exp.boot$t0 exp.tL <- linear.approx(exp.boot, L = exp.L) hist(exp.tL, nclass = 50, probability = TRUE) exp.t0 <- c(0, sqrt(var(exp.boot$t))) exp.sp <- saddle.distn(A = exp.L/12,wdist = "m", t0 = exp.t0) # The saddlepoint approximation in this case is to the density of # t-t0 and so t0 must be added for the plot. lines(exp.sp, h = function(u, t0) u+t0, J = function(u, t0) 1, t0 = exp.boot$t0)