Calculate Effective Sample Size for Single Weight Vector

Computes the effective sample size (ESS) for a single weighting scheme. This is a wrapper around ess() that follows the bal_*() naming convention for API consistency.

Usage

bal_ess(.weights, na.rm = FALSE)

Arguments

.weights

A numeric vector of weights or a single weight column from a data frame.

na.rm

A logical value indicating whether to remove missing values before computation. If FALSE (default), missing values in the input will produce NA in the output.

Value

A single numeric value representing the effective sample size.

Details

The effective sample size (ESS) is calculated using the classical formula: \(ESS = (\sum w)^2 / \sum(w^2)\).

ESS reflects how many observations you would have if all were equally weighted. When weights vary substantially, the ESS can be much smaller than the actual number of observations, indicating that a few observations carry disproportionately large weights.

Diagnostic Value:

• A large discrepancy between ESS and the actual sample size indicates that a few observations carry disproportionately large weights

• A small ESS signals that weighted estimates are more sensitive to a handful of observations, inflating the variance and standard errors

• If ESS is much lower than the total sample size, consider investigating why some weights are extremely large or small

See also

ess() for the underlying implementation, check_ess() for computing ESS across multiple weighting schemes

Other balance functions: bal_corr(), bal_ks(), bal_model_auc(), bal_model_roc_curve(), bal_qq(), bal_smd(), bal_vr(), check_balance(), check_ess(), check_model_auc(), check_model_roc_curve(), check_qq(), plot_balance()

Examples

# ESS for ATE weights
bal_ess(nhefs_weights$w_ate)
#> [1] 1013.312

# ESS for ATT weights
bal_ess(nhefs_weights$w_att)
#> [1] 1069.587

# With missing values
weights_with_na <- nhefs_weights$w_ate
weights_with_na[1:5] <- NA
bal_ess(weights_with_na, na.rm = TRUE)
#> [1] 1010.012