| Title: | Parametric Bootstrapping to Control Rank Conditional Coverage |
|---|---|
| Description: | Functions to implement the parametric and non-parametric bootstrap confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>. |
| Authors: | Jean Morrison |
| Maintainer: | Jean Morrison <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2025-02-16 04:45:38 UTC |
| Source: | https://github.com/jean997/rcc |
This function implements Algorithm 3 in the paper (see Section 2.4). The user supplies individual level data and an analysis function that generates test statistics and point estimates. The function resamples individuals from the original data and recalculates test statistics and estimates in order to calculate confidence intervals.
nonpar_bs_ci(data, analysis.func, rank.func = NULL, level = 0.9, res.orig = NULL, n.rep = 1000, use.abs = TRUE, parallel = FALSE, ...)nonpar_bs_ci(data, analysis.func, rank.func = NULL, level = 0.9, res.orig = NULL, n.rep = 1000, use.abs = TRUE, parallel = FALSE, ...)
data |
An n by K matrix of data |
analysis.func |
A function that performs the analysis. This function should take exactly one argument (data) and return a list or data frame including an item called 'estimate' and an item called 'statistic'. These should both be vectors of length p, the number of parameters. |
rank.func |
A function that takes, as first argument, the statistics returned by analysis.func. The second argument should be use.abs which can take a logical value. This argument indicates if ranking should be based on signed or absolute value of the statistics. rank.func should return a list including items named 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. |
level |
Confidence level |
res.orig |
Results of applying analysis.funct to the original data if they are already available. If NULL, these will be calculated. |
n.rep |
Number of bootstrap replications |
use.abs |
Logical. Rank based on absolute value of the statistics |
parallel |
Logical. If true, use the parallel package to make use of multiple cores. |
... |
Additional parameters to pass to rank.func |
A data frame giving original estimates and statistics, confidence intervals, debiased point estimates, and rank for each parameter.
#Generate some data -- 20 parameters, 30 samples #Most problems will have many more parameters! set.seed(4e8) dat <- matrix(rnorm(n=20*30), nrow=20) #Write a function to do a t-test comparing #the first 15 samples and the last 15 samples my.analysis.func <- function(data){ x <- rep(c(1, 0), each=15) B <- t(apply(data, MARGIN=1, FUN=function(y){ f <- t.test(y~x) est <- f$estimate[2]-f$estimate[1] stat <- f$statistic return(c(est, stat)) })) B <- data.frame(B) names(B) <- c("estimate", "statistic") return(B) } #Calculate confidence intervals cis <- nonpar_bs_ci(data=dat, analysis.func=my.analysis.func, n.rep=500) head(cis)#Generate some data -- 20 parameters, 30 samples #Most problems will have many more parameters! set.seed(4e8) dat <- matrix(rnorm(n=20*30), nrow=20) #Write a function to do a t-test comparing #the first 15 samples and the last 15 samples my.analysis.func <- function(data){ x <- rep(c(1, 0), each=15) B <- t(apply(data, MARGIN=1, FUN=function(y){ f <- t.test(y~x) est <- f$estimate[2]-f$estimate[1] stat <- f$statistic return(c(est, stat)) })) B <- data.frame(B) names(B) <- c("estimate", "statistic") return(B) } #Calculate confidence intervals cis <- nonpar_bs_ci(data=dat, analysis.func=my.analysis.func, n.rep=500) head(cis)
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.
par_bs_ci(beta, se = rep(1, length(beta)), rank.func = NULL, theta = beta, level = 0.9, n.rep = 1000, use.abs = TRUE, ...)par_bs_ci(beta, se = rep(1, length(beta)), rank.func = NULL, theta = beta, level = 0.9, n.rep = 1000, use.abs = TRUE, ...)
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 |
A data frame giving original estimates and standard errors, confidence intervals, debiased point estimates, and rank for each parameter.
#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)#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)