par_bs_ci: Parametric bootstrapped confidence intervals to control RCC

In jean997/rcc: Parametric Bootstrapping to Control Rank Conditional Coverage

Description

This function implements the parametric bootstrap (see Section 2.3 of the referenced paper). The user supplies point estimates, standard errors and optionally, a ranking function.

Usage

par_bs_ci(beta, se = rep(1, length(beta)), rank.func = NULL, theta = beta,
  level = 0.9, n.rep = 1000, use.abs = TRUE, ...)

Arguments

beta	Parameter estimates
se	Estimated standard error of beta. Defaults to 1.
rank.func	A function that takes as first argument the t-statistics beta/se and returns a list with items order and rank. See rcc:::basic_rank for an example. If NULL, the basic_rank function will be used which ranks based on the size of the test statistics.
theta	Possibly shrunken estimates of E[beta]. Defaults to beta.
level	Confidence level
n.rep	Number of bootstrap replications
use.abs	Base the rank on abs(beta) rather than beta
...	Additional parameters to pass to rank.func

Value

A data frame giving original estimates and standard errors, confidence intervals, debiased point estimates, and rank for each parameter.

Examples

#generate 100 fake parameter estimates
theta <- c(rep(0, 90), rnorm(n=10)) #vector of means
beta <- rnorm(n=100, mean=theta, sd=1)
cis <- par_bs_ci(beta=beta, n.rep=500) #calculate parametric bootstrap confidence intervals
head(cis)

jean997/rcc documentation built on May 18, 2019, 11:44 p.m.