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 "saddle.distn" (see
saddle.distn.object representing a saddlepoint
approximation to a distribution.

dens 
A logical variable indicating whether the saddlepoint density
(TRUE ; the default) or the saddlepoint distribution function
(FALSE ) should be plotted.

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 dens=TRUE this function specifies the Jacobian for any
transformation that may be necessary. The first argument of J
must the value at which the approximation is being performed and the
function must be vectorized. If h is supplied J must
also be supplied and both must have the same argument list.

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 h and J .

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) (n1)/n*var(d[i]) } exp.boot < boot(expdata,vfun, R = 9999) exp.L < (expdatamean(expdata))^2  exp.boot$t0 exp.tL < linear.approx(exp.boot, L = exp.L) hist(exp.tL, nclass = 50, prob = TRUE) exp.t0 < c(0,sqrt(var(exp.boot$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 # tt0 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)