Bootstrap confidence intervals
Bootstrap confidence intervals - percentile method or t interval.
CI.percentile(x, probs = c(0.025, 0.975), expand = TRUE, ...) CI.t(x, probs = c(0.025, 0.975)) CI.bca(x, probs = c(0.025, 0.975), expand = TRUE, L = NULL, ...) CI.bootstrapT(x, probs = c(0.025, 0.975))
probability values, between 0 and 1. The default vector
c(0.025, 0.975)gives a 95% two-sided interval.
TRUEthen use modified percentiles for better small-sample accuracy.
additional arguments to pass to
- vector of length
n, empirical influence function values. If not supplied this is computed using
CI.bootstrapT assumes the first dimension of the statistic
is an estimate, and the second is proportional to a SE for the
estimate. E.g. for bootstrapping the mean, they could be the mean and s.
This is subject to change.
CI.bootstrapT currently only support
a single sample.
a matrix with one column for each value in
probsand one row for each statistic.
This discusses the expanded percentile interval: Hesterberg, Tim (2014), What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, http://arxiv.org/abs/1411.5279.
## Not run: # # See full set of examples in resample-package, including different # # ways to call all four functions depending on the structure of the data. # data(Verizon) # CLEC <- with(Verizon, Time[Group == "CLEC"]) # bootC <- bootstrap(CLEC, mean, seed = 0) # bootC2 <- bootstrap(CLEC, c(mean = mean(CLEC), sd = sd(CLEC)), seed = 0) # CI.percentile(bootC) # CI.t(bootC) # CI.bca(bootC) # CI.bootstrapT(bootC2) # ## End(Not run)