| two.boot {simpleboot} | R Documentation |
two.boot is used to bootstrap the difference between various
univariate statistics. An example is the difference of means.
Bootstrapping is
done by independently resampling from sample1 and sample2.
two.boot(sample1, sample2, FUN, R, student = FALSE, M, weights = NULL, ...)
sample1 |
First sample; a vector of numbers. |
sample2 |
Second sample; a vector of numbers. |
FUN |
The statistic which is applied to each sample. This can be a quoted string or a function name. |
R |
Number of bootstrap replicates. |
student |
Should we do a studentized bootstrap? This requires a double bootstrap so it might take longer. |
M |
If student is set to TRUE, then M is the
number of internal bootstrap replications to do. |
weights |
Resampling weights; a list with two components. The
first component of the list is a vector of weights for
sample1 and the second component of the list is a vector of
weights for sample2. |
... |
Other (named) arguments that should be passed to
FUN. |
The differences are always taken as FUN(sample1) -
FUN(sample2). If you want the difference to be reversed you need
to reverse the order of the arguments sample1 and
sample2.
An object of class "simpleboot", which is almost identical to the
regular "boot" object. For example, the boot.ci
function can be used on this object.
Roger D. Peng
set.seed(50)
x <- rnorm(100, 1) ## Mean 1 normals
y <- rnorm(100, 0) ## Mean 0 normals
b <- two.boot(x, y, median, R = 1000)
boot.ci(b) ## No studentized confidence intervals
hist(b) ## Histogram of the bootstrap replicates
b <- two.boot(x, y, quantile, R = 1000, probs = .75)
## With weighting
## Here all members of the first group has equal weighting
## but members of the the second have unequal weighting
w <- list(rep(1, 100), 100:1)
bw <- two.boot(x, y, median, R = 1000, weights = w)
boot.ci(b)
## Studentized
bstud <- two.boot(x, y, median, R = 500, student = TRUE, M = 50)
boot.ci(bstud, type = "stud")
## Studentized with weights
bwstud <- two.boot(x, y, median, R = 500, student = TRUE, M = 50,
weights = w)
boot.ci(bstud, type = "stud")