# saddle

##### Saddlepoint Approximations for Bootstrap Statistics

This function calculates a saddlepoint approximation to the
distribution of a linear combination of **W** at a particular point
`u`

, where **W** is a vector of random variables. The
distribution of **W** may be multinomial (default), Poisson or
binary. Other distributions are possible also if the adjusted
cumulant generating function and its second derivative are given.
Conditional saddlepoint approximations to the distribution of one
linear combination given the values of other linear combinations of
**W** can be calculated for **W** having binary or Poisson
distributions.

- Keywords
- smooth, nonparametric

##### Usage

```
saddle(A = NULL, u = NULL, wdist = "m", type = "simp", d = NULL,
d1 = 1, init = rep(0.1, d), mu = rep(0.5, n), LR = FALSE,
strata = NULL, K.adj = NULL, K2 = NULL)
```

##### Arguments

- A
A vector or matrix of known coefficients of the linear combinations of

**W**. It is a required argument unless`K.adj`

and`K2`

are supplied, in which case it is ignored.- u
The value at which it is desired to calculate the saddlepoint approximation to the distribution of the linear combination of

**W**. It is a required argument unless`K.adj`

and`K2`

are supplied, in which case it is ignored.- wdist
The distribution of

**W**. This can be one of`"m"`

(multinomial),`"p"`

(Poisson),`"b"`

(binary) or`"o"`

(other). If`K.adj`

and`K2`

are given`wdist`

is set to`"o"`

.- type
The type of saddlepoint approximation. Possible types are

`"simp"`

for simple saddlepoint and`"cond"`

for the conditional saddlepoint. When`wdist`

is`"o"`

or`"m"`

,`type`

is automatically set to`"simp"`

, which is the only type of saddlepoint currently implemented for those distributions.- d
This specifies the dimension of the whole statistic. This argument is required only when

`wdist = "o"`

and defaults to 1 if not supplied in that case. For other distributions it is set to`ncol(A)`

.- d1
When

`type`

is`"cond"`

this is the dimension of the statistic of interest which must be less than`length(u)`

. Then the saddlepoint approximation to the conditional distribution of the first`d1`

linear combinations given the values of the remaining combinations is found. Conditional distribution function approximations can only be found if the value of`d1`

is 1.- init
Used if

`wdist`

is either`"m"`

or`"o"`

, this gives initial values to`nlmin`

which is used to solve the saddlepoint equation.- mu
The values of the parameters of the distribution of

**W**when`wdist`

is`"m"`

,`"p"`

`"b"`

.`mu`

must be of the same length as W (i.e.`nrow(A)`

). The default is that all values of`mu`

are equal and so the elements of**W**are identically distributed.- LR
If

`TRUE`

then the Lugananni-Rice approximation to the cdf is used, otherwise the approximation used is based on Barndorff-Nielsen's r*.- strata
The strata for stratified data.

- K.adj
The adjusted cumulant generating function used when

`wdist`

is`"o"`

. This is a function of a single parameter,`zeta`

, which calculates`K(zeta)-u%*%zeta`

, where`K(zeta)`

is the cumulant generating function of**W**.- K2
This is a function of a single parameter

`zeta`

which returns the matrix of second derivatives of`K(zeta)`

for use when`wdist`

is`"o"`

. If`K.adj`

is given then this must be given also. It is called only once with the calculated solution to the saddlepoint equation being passed as the argument. This argument is ignored if`K.adj`

is not supplied.

##### Details

If `wdist`

is `"o"`

or `"m"`

, the saddlepoint equations
are solved using `nlmin`

to minimize `K.adj`

with respect to
its parameter `zeta`

. For the Poisson and binary cases, a
generalized linear model is fitted such that the parameter estimates
solve the saddlepoint equations. The response variable 'y' for the
`glm`

must satisfy the equation `t(A)%*%y = u`

(`t()`

being the transpose function). Such a vector can be found as a feasible
solution to a linear programming problem. This is done by a call to
`simplex`

. The covariate matrix for the `glm`

is given by
`A`

.

##### Value

A list consisting of the following components

The saddlepoint approximations. The first value is the density approximation and the second value is the distribution function approximation.

The solution to the saddlepoint equation. For the conditional saddlepoint this is the solution to the saddlepoint equation for the numerator.

If `type`

is `"cond"`

this is the solution to the
saddlepoint equation for the denominator. This component is not
returned for any other value of `type`

.

##### References

Booth, J.G. and Butler, R.W. (1990) Randomization distributions and
saddlepoint approximations in generalized linear models.
*Biometrika*, **77**, 787--796.

Canty, A.J. and Davison, A.C. (1997) Implementation of saddlepoint
approximations to resampling distributions.
*Computing Science and Statistics; Proceedings of the 28th Symposium on the Interface*, 248--253.

Davison, A.C. and Hinkley, D.V. (1997)
*Bootstrap Methods and their Application*. Cambridge University Press.

Jensen, J.L. (1995) *Saddlepoint Approximations*. Oxford University Press.

##### See Also

##### Examples

```
# NOT RUN {
# To evaluate the bootstrap distribution of the mean failure time of
# air-conditioning equipment at 80 hours
saddle(A = aircondit$hours/12, u = 80)
# Alternatively this can be done using a conditional poisson
saddle(A = cbind(aircondit$hours/12,1), u = c(80, 12),
wdist = "p", type = "cond")
# To use the Lugananni-Rice approximation to this
saddle(A = cbind(aircondit$hours/12,1), u = c(80, 12),
wdist = "p", type = "cond",
LR = TRUE)
# Example 9.16 of Davison and Hinkley (1997) calculates saddlepoint
# approximations to the distribution of the ratio statistic for the
# city data. Since the statistic is not in itself a linear combination
# of random Variables, its distribution cannot be found directly.
# Instead the statistic is expressed as the solution to a linear
# estimating equation and hence its distribution can be found. We
# get the saddlepoint approximation to the pdf and cdf evaluated at
# t = 1.25 as follows.
jacobian <- function(dat,t,zeta)
{
p <- exp(zeta*(dat$x-t*dat$u))
abs(sum(dat$u*p)/sum(p))
}
city.sp1 <- saddle(A = city$x-1.25*city$u, u = 0)
city.sp1$spa[1] <- jacobian(city, 1.25, city.sp1$zeta.hat) * city.sp1$spa[1]
city.sp1
# }
```

*Documentation reproduced from package boot, version 1.3-24, License: Unlimited*