Use a weighted survey as the calibration reference instead of a frame

Wraps a reference-survey microdata data.frame together with its design weights so it can be passed as the population argument of step_model_calibration() (and any step that takes a population frame). The calibration totals are then the weighted sums over the reference survey – an estimate of the population totals – instead of unweighted sums over a full frame. This is the model-assisted / two-survey setup: fit the model on your sample, project it onto a larger reference survey, and calibrate to the weighted totals of the projection (Wu and Sitter 2001; Kim and Rao 2012).

Usage

reference_sample(data, weights, replicates = NULL)

Arguments

data

a data.frame of reference-survey microdata, with the columns used in x_formula and the model predictors.

weights

either the name (string) of a positive weight column in data, or a numeric vector with one weight per row.

replicates

optional numeric matrix (or data.frame) of replicate weights for the reference survey – one row per reference unit, one column per replicate – used to propagate the reference sampling variance through bootstrap_weights(). NULL (default) treats the totals as fixed. Note that only bootstrap_weights() pairs the reference replicates and propagates this variance; jackknife_weights() treats the estimated totals as fixed even when replicates is supplied, so use the bootstrap when this component matters.

Value

data tagged so that step_model_calibration() weights its totals by weights. It is still an ordinary data.frame.

Details

A reference survey with all weights equal to 1 reproduces the plain-frame behaviour exactly. To propagate the reference survey's own sampling variance into the recipe-aware bootstrap, pass its replicate weights through replicates: each bootstrap replicate then re-estimates the totals from the paired reference replicate (Opsomer and Erciulescu 2021), so the extra variance from estimating the totals is captured. Without replicates the totals are treated as fixed (a reasonable approximation when the reference is much larger than the sample, and the same assumption made when calibrating to another survey's published totals).

See also

step_model_calibration()

Examples

ref <- reference_sample(population, weights = rep(1, nrow(population)))