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Truncate (Winsorize) Propensity Scores

Source: R/ps_trunc.R
ps_trunc.Rd

ps_trunc() sets out‐of‐range propensity scores to fixed bounding values (a form of winsorizing). This is an alternative to ps_trim(), which removes (sets NA) instead of bounding and is then refit with ps_refit()

Usage

ps_trunc(
  ps,
  method = c("ps", "pctl", "cr"),
  lower = NULL,
  upper = NULL,
  .exposure = NULL,
  .treated = NULL,
  .untreated = NULL
)

Arguments

ps

The propensity score, a numeric vector between 0 and 1.

method

One of "ps", "pctl", or "cr".

  • "ps": directly cut on [lower, upper] of ps.

  • "pctl": use quantiles of ps as bounding values

  • "cr": the common range of ps given .exposure, bounding [min(ps[treated]), max(ps[untreated])]

lower, upper

Numeric or quantile bounds. If NULL, defaults vary by method.

.exposure

For method "cr", a binary exposure vector.

.treated

The value representing the treatment group. If not provided, it is automatically detected.

.untreated

The value representing the control group. If not provided, it is automatically detected.

Value

A ps_trunc object (numeric vector). It has an attribute ps_trunc_meta storing fields like method, lower_bound, and upper_bound.

Details

For each \(ps[i]\):

  • If \(ps[i] < lower\_bound\), we set \(ps[i] = lower\_bound\).

  • If \(ps[i] > upper\_bound\), we set \(ps[i] = upper\_bound\). This approach is often called winsorizing.

See also

ps_trim() and ps_refit() for removing extreme values vs. bounding

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")

# truncate just the 99th percentile
ps_trunc(ps, method = "pctl", lower = 0, upper = .99)
#> <ps_trunc{[0.341443426776033,0.805793268892769], method=pctl}[30]>
#>         1         2         3         4         5         6         7         8 
#> 0.5149714 0.6361298 0.7694837 0.4880712 0.6073989 0.6305169 0.6899234 0.5897388 
#>         9        10        11        12        13        14        15        16 
#> 0.8003122 0.6009455 0.6605909 0.7162599 0.5725720 0.4985231 0.7849940 0.3561684 
#>        17        18        19        20        21        22        23        24 
#> 0.7064972 0.6200818 0.7191624 0.6620999 0.8057933 0.4800637 0.7696302 0.7981060 
#>        25        26        27        28        29        30 
#> 0.6167236 0.3414434 0.6667225 0.5494305 0.6981704 0.6472363