Post stratification estimator helper

Predicts each row's outcome from a fitted model, then averages the predictions within each group using the post-stratification weights. In the book, the rows are demographic cells and the groups are states.

Usage

post_stratification_helper(model_fit, data, group, weights)

Arguments

model_fit

A fitted model that marginaleffects::predictions() accepts, e.g. from lme4::glmer() or estimatr::lm_robust(). For a mixed model, random effects are left out of the predictions (re.form = NA); for any other model no such argument is passed.

data

A data frame of post-stratification cells to predict for.

group

The bare column name of the grouping variable.

weights

The bare column name of the post-stratification weights, e.g. each cell's population share.

Value

A data frame with one row per group and columns for the group and estimate.

Details

See https://book.declaredesign.org/observational-descriptive.html#multi-level-regression-and-poststratification

Examples

set.seed(343)
# Post-stratification cells: high school graduates and non-graduates in ten
# states, weighted by each state's share of graduates
cells <- expand.grid(state = state.name[1:10], HS = 0:1)
prob_HS <- rep(seq(0.5, 0.9, length.out = 10), 2)
cells$PS_weight <- ifelse(cells$HS == 1, prob_HS, 1 - prob_HS)

survey <- cells[sample(nrow(cells), 1000, replace = TRUE), ]
survey$Y <- rbinom(1000, 1, prob = plogis(0.5 * survey$HS))

model_fit <- lme4::glmer(Y ~ HS + (1 | state), data = survey, family = binomial)
post_stratification_helper(model_fit, data = cells, group = state, weights = PS_weight)
#> # A tibble: 10 × 2
#>    state       estimate
#>    <fct>          <dbl>
#>  1 Alabama        0.560
#>  2 Alaska         0.565
#>  3 Arizona        0.569
#>  4 Arkansas       0.574
#>  5 California     0.578
#>  6 Colorado       0.583
#>  7 Connecticut    0.587
#>  8 Delaware       0.592
#>  9 Florida        0.596
#> 10 Georgia        0.601