jackknife_estimate: Jackknife estimate, standard error and confidence interval

In weightflow: Declarative API for Staged Survey Weights

Jackknife estimate, standard error and confidence interval

Description

Applies a statistic to the point weights and to every delete-a-PSU replicate, and summarises it with the stratified jackknife (JKn) variance

\sum_h \frac{n_h - 1}{n_h} \sum_{i \in h} (\theta_{(hi)} - \bar\theta_h)^2,

where \theta_{(hi)} is the estimate with PSU i of stratum h deleted and \bar\theta_h the mean of those over the stratum. No finite population correction is applied.

Usage

jackknife_estimate(jack, statistic, level = 0.95)

jack_total(jack, variable)

jack_mean(jack, variable)

Arguments

jack	a weightflow_jack object.
statistic	a function ⁠function(w, data)⁠ returning a numeric scalar (or vector) given a weight vector and the data.
level	confidence level for the (normal) interval.
variable	name of the variable to estimate (for jack_total/jack_mean).

Value

A data frame with estimate, se, ci_lower, ci_upper.

Examples

spec <- weighting_spec(sample_one, base_weights = pw) |>
  step_calibrate(method = "raking",
                 margins = list(region = c(table(population$region))))
jk <- jackknife_weights(spec, strata = "region", psu = "psu", progress = FALSE)
jackknife_estimate(jk, function(w, d) sum(w * d$employed, na.rm = TRUE))

weightflow documentation built on July 23, 2026, 1:08 a.m.