riskdiff (version 0.2.1)

create_balance_plots: Create Balance Plots for IPTW Analysis

Description

Creates visualizations to assess covariate balance before and after IPTW weighting. Includes love plots (standardized differences) and propensity score distribution plots.

Usage

create_balance_plots(
  iptw_result,
  plot_type = "both",
  threshold = 0.1,
  save_plots = FALSE,
  plot_dir = "plots"
)

Value

A ggplot object (if plot_type is "love" or "ps") or a list of ggplot objects (if plot_type is "both"). If ggplot2 is not available, returns a message and creates base R plots.

Arguments

iptw_result

An iptw_result object from calc_iptw_weights()

plot_type

Type of plot: "love" for standardized differences, "ps" for propensity score distributions, or "both"

threshold

Threshold for acceptable standardized difference (default: 0.1)

save_plots

Whether to save plots to files (default: FALSE)

plot_dir

Directory to save plots if save_plots=TRUE (default: "plots")

Details

Love Plot

Shows standardized differences for each covariate before and after weighting. Points represent standardized differences, with lines connecting before/after values. Horizontal lines show common thresholds (0.1, 0.25) for acceptable balance.

Propensity Score Plot

Shows distributions of propensity scores by treatment group before and after weighting. Good overlap indicates positivity assumption is met.

Examples

# \donttest{
data(cachar_sample)

# Calculate IPTW weights
iptw_result <- calc_iptw_weights(
  data = cachar_sample,
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking")
)

# Create balance plots
if (requireNamespace("ggplot2", quietly = TRUE)) {
  plots <- create_balance_plots(iptw_result, plot_type = "both")
  print(plots$love_plot)
  print(plots$ps_plot)
}
# }