Using the normal approximation to a statistic, calculate equi-tailed two-sided confidence intervals.
norm.ci(boot.out = NULL, conf = 0.95, index = 1, var.t0 = NULL, t0 = NULL, t = NULL, L = NULL, h = function(t) t, hdot = function(t) 1, hinv = function(t) t)
- A bootstrap output object returned from a call to
t0is missing then
boot.outis a required argument. It is also required if both
- A scalar or vector containing the confidence level(s) of the required interval(s).
- The index of the statistic of interest within the output of a call to
boot.out$statistic. It is not used if
boot.outis missing, in which case
t0must be supplied.
- The variance of the statistic of interest. If it is not supplied then
- The observed value of the statistic of interest. If it is missing then it is taken from
boot.outwhich is required in that case.
- Bootstrap replicates of the variable of interest. These are used to estimate the variance of the statistic of interest if
var.t0is not supplied. The default value is
- The empirical influence values for the statistic of interest. These are used to calculate
boot.outare supplied. If a transformation is supplied through
hthen the influence values must be for the untransformed statistic
- A function defining a monotonic transformation, the intervals are calculated on the scale of
h(t)and the inverse function
hinvis applied to the resulting intervals.
hmust be a function of one variable only and must be vectorized. The default is the identity function.
- A function of one argument returning the derivative of
h. It is a required argument if
his supplied and is used for approximating the variance of
h(t0). The default is the constant function 1.
- A function, like
h, which returns the inverse of
h. It is used to transform the intervals calculated on the scale of
h(t)back to the original scale. The default is the identity function. If
his supplied but
hinvis not, then the intervals returned will be on the transformed scale.
It is assumed that the statistic of interest has an approximately normal distribution with variance
var.t0 and so a confidence interval of length
2*qnorm((1+conf)/2)*sqrt(var.t0) is found. If
t are supplied then the interval is bias-corrected using the bootstrap bias estimate, and so the interval would be centred at
2*t0-mean(t). Otherwise the interval is centred at
length(conf) is 1 then a vector containing the confidence level and the endpoints of the interval is returned. Otherwise, the returned value is a matrix where each row corresponds to a different confidence level.
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
This function is primarily designed to be called by
boot.ci to calculate the normal approximation after a bootstrap but it can also be used without doing any bootstrap calculations as long as
var.t0 can be supplied. See the examples below.
# In Example 5.1 of Davison and Hinkley (1997), normal approximation # confidence intervals are found for the air-conditioning data. air.mean <- mean(aircondit$hours) air.n <- nrow(aircondit) air.v <- air.mean^2/air.n norm.ci(t0 = air.mean, var.t0 = air.v) exp(norm.ci(t0 = log(air.mean), var.t0 = 1/air.n)[2:3]) # Now a more complicated example - the ratio estimate for the city data. ratio <- function(d, w) sum(d$x * w)/sum(d$u *w) city.v <- var.linear(empinf(data = city, statistic = ratio)) norm.ci(t0 = ratio(city,rep(0.1,10)), var.t0 = city.v)
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