print.resample {resample} | R Documentation |
Methods for common generic functions for resample objects
Description
Methods for common generic functions. The methods operate primarily on the replicates (resampled statistics).
Usage
## S3 method for class 'resample'
print(x, ...)
## S3 method for class 'resample'
hist(x, ..., resampleColumns = 1:x$p, xlim = NULL,
xlab = NULL, main = "", col = "blue", border = 0,
breaks = "FD", showObserved = TRUE,
legend = TRUE, args.legend = NULL)
## S3 method for class 'resample'
plot(x, ...)
## S3 method for class 'resample'
qqnorm(y, ..., resampleColumns = 1:y$p, ylab = NULL,
pch = if(y$R < 100) 1 else ".")
## S3 method for class 'resample'
quantile(x, ...)
Arguments
x , y |
a |
... |
additional arguments passed to the corresponding generic function. |
resampleColumns |
integer subscripts, or names of statistics. When a statistic is a vector, resampleColumns may be used to select which resampling distributions to plot. |
xlim |
limits for the x axis. |
xlab , ylab |
x and y axis labels. |
main |
main title |
col |
color used to fill bars, see |
border |
color of the order around the bars, see |
breaks |
method for computing breaks, see |
showObserved |
logical, if |
legend |
logical, if |
args.legend |
|
pch |
plotting character, see |
Details
hist.resample
displays a histogram overlaid with a density
plot, with the observed value of the statistic indicated.
plot.resample
currently just calls hist.resample
.
Value
For quantile.resample
, a matrix with one row for each
statistic and one column for each value in probs
.
This uses type=6
when calling
quantile
, for wider (more accurate) quantiles than
the usual default.
The other functions are not called for their return values.
Author(s)
Tim Hesterberg timhesterberg@gmail.com,
https://www.timhesterberg.net/bootstrap-and-resampling
See Also
resample-package
,
bootstrap
,
bootstrap2
,
jackknife
,
permutationTest
,
permutationTest2
,
quantile
.
Examples
# See full set of examples in resample-package
data(Verizon)
CLEC <- with(Verizon, Time[Group == "CLEC"])
bootC <- bootstrap(CLEC, mean, seed = 0)
print(bootC)
hist(bootC)
qqnorm(bootC)
quantile(bootC, probs = c(.25, .975))
# That is the percentile interval with expand = FALSE
CI.percentile(bootC)