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paf2.boot <- function(y, a, R = 1000, ncores = 1) {
index <- DER::paf2(y, a, ncores)
boot <- matrix(0, R, 3)
n <- length(y)
for (i in 1:R) {
boot[i, ] <- DER::paf2(y[Rfast2::Sample.int(n, n, replace = TRUE)], a, ncores)
}
colnames(boot) <- c("paf", "deprivation", "surplus")
mesoi <- Rfast::colmeans(boot)
bias <- index - mesoi
se <- Rfast::colVars(boot, std = TRUE)
ci <- Rfast2::colQuantile( boot, probs = c(0.025, 0.975) )
info <- rbind(mesoi, bias, se, ci)
colnames(info) <- colnames(boot)
rownames(info) <- c("mesoi", "bias", "se", "2.5%", "97.5%" )
list(boot = boot, index = index, info = info)
}
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