smooth.f {boot} | R Documentation |

## Smooth Distributions on Data Points

### Description

This function uses the method of frequency smoothing to find a distribution
on a data set which has a required value, `theta`

, of the statistic of
interest. The method results in distributions which vary smoothly with
`theta`

.

### Usage

```
smooth.f(theta, boot.out, index = 1, t = boot.out$t[, index],
width = 0.5)
```

### Arguments

`theta` |
The required value for the statistic of interest. If |

`boot.out` |
A bootstrap output object returned by a call to |

`index` |
The index of the variable of interest in the output of |

`t` |
The bootstrap values of the statistic of interest. This must be a vector of
length |

`width` |
The standardized width for the kernel smoothing. The smoothing uses a
value of |

### Details

The new distributional weights are found by applying a normal kernel smoother
to the observed values of `t`

weighted by the observed frequencies in the
bootstrap simulation. The resulting distribution may not have
parameter value exactly equal to the required value `theta`

but it will
typically have a value which is close to `theta`

. The details of how this
method works can be found in Davison, Hinkley and Worton (1995) and Section
3.9.2 of Davison and Hinkley (1997).

### Value

If `length(theta)`

is 1 then a vector with the same length as the data set
`boot.out$data`

is returned. The value in position `i`

is the probability
to be given to the data point in position `i`

so that the distribution has
parameter value approximately equal to `theta`

.
If `length(theta)`

is bigger than 1 then the returned value is a matrix with
`length(theta)`

rows each of which corresponds to a distribution with the
parameter value approximately equal to the corresponding value of `theta`

.

### References

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

Davison, A.C., Hinkley, D.V. and Worton, B.J. (1995) Accurate and efficient
construction of bootstrap likelihoods. *Statistics and Computing*,
**5**, 257–264.

### See Also

### Examples

```
# Example 9.8 of Davison and Hinkley (1997) requires tilting the resampling
# distribution of the studentized statistic to be centred at the observed
# value of the test statistic 1.84. In the book exponential tilting was used
# but it is also possible to use smooth.f.
grav1 <- gravity[as.numeric(gravity[, 2]) >= 7, ]
grav.fun <- function(dat, w, orig) {
strata <- tapply(dat[, 2], as.numeric(dat[, 2]))
d <- dat[, 1]
ns <- tabulate(strata)
w <- w/tapply(w, strata, sum)[strata]
mns <- as.vector(tapply(d * w, strata, sum)) # drop names
mn2 <- tapply(d * d * w, strata, sum)
s2hat <- sum((mn2 - mns^2)/ns)
c(mns[2] - mns[1], s2hat, (mns[2]-mns[1]-orig)/sqrt(s2hat))
}
grav.z0 <- grav.fun(grav1, rep(1, 26), 0)
grav.boot <- boot(grav1, grav.fun, R = 499, stype = "w",
strata = grav1[, 2], orig = grav.z0[1])
grav.sm <- smooth.f(grav.z0[3], grav.boot, index = 3)
# Now we can run another bootstrap using these weights
grav.boot2 <- boot(grav1, grav.fun, R = 499, stype = "w",
strata = grav1[, 2], orig = grav.z0[1],
weights = grav.sm)
# Estimated p-values can be found from these as follows
mean(grav.boot$t[, 3] >= grav.z0[3])
imp.prob(grav.boot2, t0 = -grav.z0[3], t = -grav.boot2$t[, 3])
# Note that for the importance sampling probability we must
# multiply everything by -1 to ensure that we find the correct
# probability. Raw resampling is not reliable for probabilities
# greater than 0.5. Thus
1 - imp.prob(grav.boot2, index = 3, t0 = grav.z0[3])$raw
# can give very strange results (negative probabilities).
```

*boot*version 1.3-30 Index]