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February 7, 2022 21:38
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Bayesian bootstrap in `R` with `update()`
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library(fixest) | |
library(data.table) | |
library(insight) | |
bboot_vab <- function(model, reps = 1e3, conf.level = .95, cluster = NULL) { | |
data <- insight::get_data(model) | |
setDT(data) | |
if (!is.null(cluster)) { | |
if (anyNA(data[[cluster]])) { | |
stop("The cluster variable cannot include missing values.") | |
} | |
cluster_idx <- unique(data[[cluster]]) | |
wts <- rexp(reps * length(cluster_idx)) | |
wts <- matrix(wts, ncol = reps) | |
wts <- data.table(cluster_idx, wts) | |
colnames(wts)[1] <- cluster | |
wts <- merge(data, wts, all.x = TRUE, sort = FALSE) | |
wts <- as.matrix(wts[, (ncol(data)+1):ncol(wts)]) } else { | |
wts <- rexp(reps * nrow(data)) | |
wts <- matrix(wts, ncol = reps) | |
} | |
fit <- function(i) { | |
coef(update(model, weights = wts[, i])) | |
} | |
boot_mat <- t(sapply(seq_len(reps), fit)) | |
alpha <- 1 - conf.level | |
ci <- c(alpha / 2, 1 - alpha / 2) | |
out <- t(apply(boot_mat, 2, \(x) c(mean(x), quantile(x, ci)))) | |
return(out) | |
} | |
model <- fepois(Sepal.Length ~ Sepal.Width + Petal.Length | Species, | |
data = iris) | |
bboot_vab(model, cluster = "Species") |
Won't ncol(data)+1
be out of range?
I don't think so, as in line 17 you merge wts
to data. So in effect with (ncol(data)+1):ncol(wts)
you only retain the wts part.
Ah, got it! Read too quickly.
To be fair, this is something I wrote in 10 minutes as a way to understand this thing. I didn't expect anyone to look at it it ;)
Made the change.
:) It does the job!
Follow up question that I have just now been looking into: Any intuition on how to allow for multiway clustering?
Maybe line 12 you would create a unique ID for each combination of your various cluster variables? A row-wise apply(data[[cluster_variables]], 1, paste)
would work, maybe?
Not sure whether that does the trick. I'll have a look sometime, if I find something, I'll post it here!
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Line 18 can be a problem when more than one set of fixed effects is included. More flexible: