snowboot (version 0.5.0)

bootCI: Build Bootstrap Confidence Intervals for $\hat{p_k}$

Description

The function will build bootstrap confidence intervals for the bootstrap estimate of and $\mu$ with a lower-bound of 0.025 and an upper-bound of 0.975.

Usage

bootCI(outBootdeg, bootstrap_mean = T, lower_bound = 0.025,
  upper_bound = 0.975)

Arguments

outBootdeg

A list that is the output of bootdeg

bootstrap_mean

A Boolean option to return the bootstrap confidence interval for the mean.

lower_bound

The lower quantile for the bootstrap confidence intervals.

upper_bound

The upper quantile for the bootstrap confidence intervals.

Value

• A list of two elements
• p_k_CI
  This a list of length length(outBootdeg$num.sam), one element per LSMI. Each element contains three sets of bootstrap confidence intervals for $\hat{p}_k^*$ corresponding to the three estimation methods. See bootdeg for more on the three estimation methods.
• mean_CI
  This a list of length length(outBootdeg$num.sam), one element per LSMI. Each element contains three sets of bootstrap confidence intervals for $\hat{\mu}$ corresponding to the three estimation methods. See bootdeg for more on the three estimation methods.

References

Efron, B. (1979). Bootstrap methods: another look at the jackknife. The annals of Statistics, 1-26.

Thompson, M. E., Ramirez Ramirez, L. L., Lyubchich, V. and Gel, Y. R. (2015), Using the bootstrap for statistical inference on random graphs. Can J Statistics. doi: 10.1002/cjs.11271

Examples

net <- artificial_networks[[1]]
sam.out <- Oempdegreedistrib(net = net, n.seeds = 40, n.neigh = 1, num.sam = 1)
outBootdeg <- bootdeg(sam.out = sam.out, n.boot = 50)
a <- bootCI(outBootdeg)