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Check ROC Curves for Multiple Weights

Source: R/check_model_auc.R
check_model_roc_curve.Rd

Computes ROC curves (weighted or unweighted) for evaluating propensity score balance. In causal inference, an ROC curve near the diagonal (AUC around 0.5) indicates good balance between treatment groups.

Usage

check_model_roc_curve(
  .data,
  .exposure,
  .estimate,
  .weights = NULL,
  include_observed = TRUE,
  na.rm = TRUE,
  .focal_level = NULL
)

Arguments

.data

A data frame containing the variables.

.exposure

The treatment/outcome variable (unquoted).

.estimate

The propensity score or covariate (unquoted).

.weights

Optional weighting variables (unquoted, can be multiple).

include_observed

Include unweighted results? Default TRUE.

na.rm

Remove missing values? Default TRUE.

.focal_level

The level of .exposure to consider as the treatment/event. Default is NULL, which uses the second level.

Value

A tibble with class "halfmoon_roc" containing ROC curve data.

See also

check_model_auc() for AUC summaries, bal_model_roc_curve() for single ROC curves

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