ESS: Compute effective sample size of weighted sample

In WeightIt: Weighting for Covariate Balance in Observational Studies

Compute effective sample size of weighted sample

Description

Computes the effective sample size (ESS) of a weighted sample, which represents the size of an unweighted sample with approximately the same amount of precision as the weighted sample under consideration.

The ESS is calculated as (\sum w)^2/\sum w^2.

Usage

ESS(w)

Arguments

w	a vector of weights

References

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies. Psychological Methods, 9(4), 403–425. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/1082-989X.9.4.403")}

Shook‐Sa, B. E., & Hudgens, M. G. (2020). Power and sample size for observational studies of point exposure effects. Biometrics, biom.13405. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/biom.13405")}

See Also

summary.weightit()

Examples

library("cobalt")
data("lalonde", package = "cobalt")

#Balancing covariates between treatment groups (binary)
(W1 <- weightit(treat ~ age + educ + married +
                  nodegree + re74, data = lalonde,
                method = "glm", estimand = "ATE"))
summary(W1)
ESS(W1$weights[W1$treat == 0])
ESS(W1$weights[W1$treat == 1])

WeightIt documentation built on Oct. 4, 2024, 9:07 a.m.