R/linearBootstrapConfidenceInterval_stageTwo.R

In twostageTE: Two-Stage Threshold Estimation

linearBootstrapConfidenceInterval_stageTwo <-
function(explanatory, response, Y_0, level=NA) {
	numBootstrap <- 1000
	if (is.na(level)) {
		level <- 0.95
	}
	alpha <- 1 - level
	n <- length(response)
	
	fit <- threshold_estimate_locLinear(explanatory, response, Y_0)
	Rn <- rep(0, numBootstrap)
	for (i in 1:numBootstrap) {
		ind <- sample(x=n, replace=TRUE)	 ## sample with replacement
		fit_bst <- threshold_estimate_locLinear(explanatory[ind], response[ind], Y_0)
		Rn[i] <- sqrt(length(ind)) * (fit$threshold_estimate_explanatory - fit_bst$threshold_estimate_explanatory)
	}
	qU <- quantile(Rn, level + alpha/2)
	qL <- quantile(Rn, alpha/2)
	uBand <- fit$threshold_estimate_explanatory  + qU / sqrt(n)
	lBand <- fit$threshold_estimate_explanatory  + qL / sqrt(n)
	if (lBand < uBand) {
		return(list(estimate=mean(c(max(lBand, min(explanatory)), min(uBand, max(explanatory)))),lower=max(lBand, min(explanatory)), upper=min(uBand, max(explanatory)), sigmaSq=NA, deriv_d0=NA))
	}
	else {
		return(list(estimate=mean(c(max(lBand, min(explanatory)), min(lBand, max(explanatory)))),lower=max(uBand, min(explanatory)), upper=min(lBand, max(explanatory)), sigmaSq=NA, deriv_d0=NA))
	}
}