Skip to contents

Create balance plot from check_balance output

Source: R/plot_balance.R
plot_balance.Rd

Create a Love plot-style visualization to assess balance across multiple metrics computed by check_balance(). This function wraps geom_love() to create a comprehensive balance assessment plot.

Usage

plot_balance(
  .df,
  abs_smd = TRUE,
  facet_scales = "free",
  linewidth = 0.8,
  point_size = 1.85,
  vline_xintercept = 0.1,
  vline_color = "grey70",
  vlinewidth = 0.6
)

Arguments

.df

A data frame produced by check_balance()

abs_smd

Logical. Take the absolute value of SMD estimates? Defaults to TRUE. Does not affect other metrics which are already non-negative.

facet_scales

Character. Scale specification for facets. Defaults to "free" to allow different scales for different metrics. Options are "fixed", "free_x", "free_y", or "free".

linewidth

The line size, passed to ggplot2::geom_line().

point_size

The point size, passed to ggplot2::geom_point().

vline_xintercept

The X intercept, passed to ggplot2::geom_vline().

vline_color

The vertical line color, passed to ggplot2::geom_vline().

vlinewidth

The vertical line size, passed to ggplot2::geom_vline().

Value

A ggplot2 object

Details

This function visualizes the output of check_balance(), creating a plot that shows balance statistics across different variables, methods, and metrics. The plot uses faceting to separate different metrics and displays the absolute value of SMD by default (controlled by abs_smd).

For categorical exposures (>2 levels), the function automatically detects multiple group level comparisons and uses facet_grid() to display each comparison in a separate row, with metrics in columns. For binary exposures, the standard facet_wrap() by metric is used.

Different metrics have different interpretations:

  • SMD: Standardized mean differences, where values near 0 indicate good balance. Often displayed as absolute values.

  • Variance Ratio: Ratio of variances between groups, where values near 1 indicate similar variability.

  • KS: Kolmogorov-Smirnov statistic, where smaller values indicate better distributional balance.

  • Correlation: For continuous exposures, measures association with covariates.

  • Energy: Multivariate balance metric applied to all variables simultaneously.

See also

check_balance() for computing balance metrics, geom_love() for the underlying geom

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_model_roc_curve(), check_qq()

Examples

# Compute balance metrics
balance_data <- check_balance(
  nhefs_weights,
  c(age, education, race),
  qsmk,
  .weights = c(w_ate, w_att)
)

# Create balance plot
plot_balance(balance_data)


# Without absolute SMD values
plot_balance(balance_data, abs_smd = FALSE)


# With fixed scales across facets
plot_balance(balance_data, facet_scales = "fixed")


# Customize threshold lines
plot_balance(balance_data, vline_xintercept = 0.05)


# Categorical exposure example
# Automatically uses facet_grid to show each group comparison
balance_cat <- check_balance(
  nhefs_weights,
  c(age, wt71, sex),
  alcoholfreq_cat,
  .weights = w_cat_ate,
  .metrics = c("smd", "vr")
)
plot_balance(balance_cat)