Refit a Propensity Score Model on Retained Observations

Re-estimates a propensity score model using only the observations retained after trimming. This is the recommended intermediate step between ps_trim() and weight calculation (e.g. wt_ate()):

ps_trim() -> ps_refit() -> wt_*()

Trimming changes the target population by removing observations with extreme propensity scores. Refitting the model on the retained subset produces propensity scores that better reflect this population, improving both model fit and downstream weight estimation. Weight functions warn if a trimmed propensity score has not been refit.

Usage

ps_refit(trimmed_ps, model, .data = NULL, ...)

Arguments

trimmed_ps

A ps_trim object returned by ps_trim().

model

The original fitted model used to estimate the propensity scores (e.g. a glm or multinom object). The model is refit via update() on the retained subset.

.data

A data frame. If NULL (the default), the data are extracted from model via model.frame().

...

Additional arguments passed to update().

Value

A ps_trim object with re-estimated propensity scores for retained observations and NA for trimmed observations. Use is_refit() to confirm refitting was applied.

See also

ps_trim() for the trimming step, is_refit() to check refit status, wt_ate() and other weight functions for the next step in the pipeline.

Examples

set.seed(2)
n <- 200
x <- rnorm(n)
z <- rbinom(n, 1, plogis(0.4 * x))

# fit a propensity score model
ps_model <- glm(z ~ x, family = binomial)
ps <- predict(ps_model, type = "response")

# trim -> refit -> weight pipeline
trimmed <- ps_trim(ps, lower = 0.1, upper = 0.9)
refit <- ps_refit(trimmed, ps_model)
wts <- wt_ate(refit, .exposure = z)
#> ℹ Treating `.exposure` as binary
#> ℹ Setting focal level to 1

is_refit(refit)
#> [1] TRUE