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Refit the Propensity Score Model on Retained Observations

Source: R/ps_trim.R
ps_refit.Rd

Takes a ps_trim object and the original model used to calculate the propensity score, then:

  1. Retrieves data from the model (or from .df argument if provided)

  2. Subsets rows to the non‐trimmed indices

  3. Refits the model

  4. Predicts new propensity scores for all rows (trimmed rows -> NA)

  5. Returns a new ps_trim object with refit = TRUE.

Usage

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

Arguments

trimmed_ps

A ps_trim object (same length as data, NAs for trimmed).

model

The fitted model used to get the original PS (e.g. a glm).

.data

Optional. A data frame. If NULL, we try to retrieve from model.

...

Additional arguments passed to update().

Value

A new ps_trim object with updated propensity scores and ps_trim_meta(x)$refit set to TRUE.

See also

ps_trim(), is_refit(), is_ps_trimmed()

Examples

set.seed(2)
n <- 30
x <- rnorm(n)
z <- rbinom(n, 1, plogis(0.4 * x))
fit <- glm(z ~ x, family = binomial)
ps <- predict(fit, type = "response")

# trim and refit
refit <- ps_trim(ps, lower = .2, upper = .8) |>
  ps_refit(fit)

is_refit(refit)
#> [1] TRUE