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Balance Variance Ratio for Two Groups

Source: R/compute_balance.R
bal_vr.Rd

Calculates the ratio of variances between two groups: var(comparison) / var(reference). For binary variables, uses the p*(1-p) variance formula. For continuous variables, uses Bessel's correction for weighted sample variance.

Usage

bal_vr(
  .covariate,
  .exposure,
  .weights = NULL,
  .reference_level = NULL,
  na.rm = FALSE
)

Arguments

.covariate

A numeric vector containing the covariate values to compare.

.exposure

A vector (factor or numeric) indicating group membership. Must have exactly two unique levels.

.weights

An optional numeric vector of case weights. If provided, must have the same length as other input vectors. All weights must be non-negative.

.reference_level

The reference group level for comparisons. Can be either a group level value or a numeric index. If NULL (default), uses the first level.

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 numeric value representing the variance ratio. Values greater than 1 indicate the comparison group has higher variance than the reference group.

Details

The variance ratio compares the variability of a covariate between treatment groups. It is calculated as: $$VR = \frac{s_1^2}{s_0^2}$$ where \(s_1^2\) and \(s_0^2\) are the variances of the treatment and control groups.

For binary variables (0/1), variance is computed as \(p(1-p)\) where \(p\) is the proportion of 1s in each group. For continuous variables, the weighted sample variance is used with Bessel's correction when weights are provided.

Values close to 1.0 indicate similar variability between groups, which is desirable for balance. Values substantially different from 1.0 suggest imbalanced variance.

See also

check_balance() for computing multiple balance metrics at once

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

Examples

# Binary exposure
bal_vr(nhefs_weights$age, nhefs_weights$qsmk)
#> [1] 1.073075

# With weights
bal_vr(nhefs_weights$wt71, nhefs_weights$qsmk,
       .weights = nhefs_weights$w_ate)
#> [1] 1.000901

# Categorical exposure (returns named vector)
bal_vr(nhefs_weights$age, nhefs_weights$alcoholfreq_cat)
#> lt_12_per_year_vs_none  1_4_per_month_vs_none   2_3_per_week_vs_none 
#>               1.271705               1.223574               1.295637 
#>          daily_vs_none 
#>               1.267799 

# Specify reference level
bal_vr(nhefs_weights$age, nhefs_weights$alcoholfreq_cat,
       .reference_level = "2_3_per_week")
#>           none_vs_2_3_per_week lt_12_per_year_vs_2_3_per_week 
#>                      0.7718213                      0.9815290 
#>  1_4_per_month_vs_2_3_per_week          daily_vs_2_3_per_week 
#>                      0.9443805                      0.9785144 

# With categorical weights
bal_vr(nhefs_weights$wt71, nhefs_weights$alcoholfreq_cat,
       .weights = nhefs_weights$w_cat_ate)
#> lt_12_per_year_vs_none  1_4_per_month_vs_none   2_3_per_week_vs_none 
#>              0.8819113              0.8610861              0.8629089 
#>          daily_vs_none 
#>              0.8990640