boot.ci

0th

Percentile

Nonparametric Bootstrap Confidence Intervals

This function generates 5 different types of equi-tailed two-sided nonparametric confidence intervals. These are the first order normal approximation, the basic bootstrap interval, the studentized bootstrap interval, the bootstrap percentile interval, and the adjusted bootstrap percentile (BCa) interval. All or a subset of these intervals can be generated.

Keywords
htest, nonparametric
Usage
boot.ci(boot.out, conf = 0.95, type = "all", 
        index = 1:min(2,length(boot.out$t0)), var.t0 = NULL, 
        var.t = NULL, t0 = NULL, t = NULL, L = NULL,
        h = function(t) t, hdot = function(t) rep(1,length(t)),
        hinv = function(t) t, ...)
Arguments
boot.out
An object of class "boot" containing the output of a bootstrap calculation.
conf
A scalar or vector containing the confidence level(s) of the required interval(s).
type
A vector of character strings representing the type of intervals required. The value should be any subset of the values c("norm","basic", "stud", "perc", "bca") or simply "all" which will compute all five types of int
index
This should be a vector of length 1 or 2. The first element of index indicates the position of the variable of interest in boot.out$t0 and the relevant column in boot.out$t. The second element indicates
var.t0
If supplied, a value to be used as an estimate of the variance of the statistic for the normal approximation and studentized intervals. If it is not supplied and length(index) is 2 then var.t0 defaults to boot.o
var.t
This is a vector (of length boot.out$R) of variances of the bootstrap replicates of the variable of interest. It is used only for studentized intervals. If it is not supplied and length(index) is 2 then var.t<
t0
The observed value of the statistic of interest. The default value is boot.out$t0[index[1]]. Specification of t0 and t allows the user to get intervals for a transformed statistic which may not be in th
t
The bootstrap replicates of the statistic of interest. It must be a vector of length boot.out$R. It is an error to supply one of t0 or t but not the other. Also if studentized intervals are required an
L
The empirical influence values of the statistic of interest for the observed data. These are used only for BCa intervals. If a transformation is supplied through the parameter h then L should be the influence values
h
A function defining a transformation. The intervals are calculated on the scale of h(t) and the inverse function hinv applied to the resulting intervals. It must be a function of one variable only and for a vector a
hdot
A function of one argument returning the derivative of h. It is a required argument if h is supplied and normal, studentized or BCa intervals are required. The function is used for approximating the variances of
hinv
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 h
...
Any extra arguments that boot.out$statistic is expecting. These arguments are needed only if BCa intervals are required and L is not supplied since in that case L is calculated through a call to emp
Details

The formulae on which the calculations are based can be found in Chapter 5 of Davison and Hinkley (1997). Function boot must be run prior to running this function to create the object to be passed as boot.out.

Variance estimates are required for studentized intervals. The variance of the observed statistic is optional for normal theory intervals. If it is not supplied then the bootstrap estimate of variance is used. The normal intervals also use the bootstrap bias correction.

Interpolation on the normal quantile scale is used when a non-integer order statistic is required. If the order statistic used is the smallest or largest of the R values in boot.out a warning is generated and such intervals should not be considered reliable.

Value

  • An object of type "bootci" which contains the intervals. It has components
  • RThe number of bootstrap replicates on which the intervals were based.
  • t0The observed value of the statistic on the same scale as the intervals.
  • callThe call to boot.ci which generated the object.

    It will also contain one or more of the following components depending on the value of type used in the call to bootci.

  • normalA matrix of intervals calculated using the normal approximation. It will have 3 columns, the first being the level and the other two being the upper and lower endpoints of the intervals.
  • basicThe intervals calculated using the basic bootstrap method.
  • studentThe intervals calculated using the studentized bootstrap method.
  • percentThe intervals calculated using the bootstrap percentile method.
  • bcaThe intervals calculated using the adjusted bootstrap percentile (BCa) method.

    These latter four components will be matrices with 5 columns, the first column containing the level, the next two containing the indices of the order statistics used in the calculations and the final two the calculated endpoints themselves.

References

Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application, Chapter 5. Cambridge University Press.

DiCiccio, T.J. and Efron B. (1996) Bootstrap confidence intervals (with Discussion). Statistical Science, 11, 189--228.

Efron, B. (1987) Better bootstrap confidence intervals (with Discussion). Journal of the American Statistical Association, 82, 171--200.

See Also

abc.ci, boot, empinf, norm.ci

Aliases
  • boot.ci
Examples
# confidence intervals for the city data
ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
city.boot <- boot(city, ratio, R = 999, stype = "w", sim = "ordinary")
boot.ci(city.boot, conf = c(0.90, 0.95),
        type = c("norm", "basic", "perc", "bca"))

# studentized confidence interval for the two sample 
# difference of means problem using the final two series
# of the gravity data. 
diff.means <- function(d, f)
{    n <- nrow(d)
     gp1 <- 1:table(as.numeric(d$series))[1]
     m1 <- sum(d[gp1,1] * f[gp1])/sum(f[gp1])
     m2 <- sum(d[-gp1,1] * f[-gp1])/sum(f[-gp1])
     ss1 <- sum(d[gp1,1]^2 * f[gp1]) - (m1 *  m1 * sum(f[gp1]))
     ss2 <- sum(d[-gp1,1]^2 * f[-gp1]) - (m2 *  m2 * sum(f[-gp1]))
     c(m1 - m2, (ss1 + ss2)/(sum(f) - 2))
}
grav1 <- gravity[as.numeric(gravity[,2]) >= 7, ]
grav1.boot <- boot(grav1, diff.means, R = 999, stype = "f",
                   strata = grav1[ ,2])
boot.ci(grav1.boot, type = c("stud", "norm"))

# Nonparametric confidence intervals for mean failure time 
# of the air-conditioning data as in Example 5.4 of Davison
# and Hinkley (1997)
mean.fun <- function(d, i) 
{    m <- mean(d$hours[i])
     n <- length(i)
     v <- (n-1)*var(d$hours[i])/n^2
     c(m, v)
}
air.boot <- boot(aircondit, mean.fun, R = 999)
boot.ci(air.boot, type = c("norm", "basic", "perc", "stud"))

# Now using the log transformation
# There are two ways of doing this and they both give the
# same intervals.

# Method 1
boot.ci(air.boot, type = c("norm", "basic", "perc", "stud"), 
        h = log, hdot = function(x) 1/x)

# Method 2
vt0 <- air.boot$t0[2]/air.boot$t0[1]^2
vt <- air.boot$t[, 2]/air.boot$t[ ,1]^2
boot.ci(air.boot, type = c("norm", "basic", "perc", "stud"), 
        t0 = log(air.boot$t0[1]), t = log(air.boot$t[,1]),
        var.t0 = vt0, var.t = vt)
Documentation reproduced from package boot, version 1.3-9, License: Unlimited

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