Creates comprehensive covariate balance plots showing standardized mean differences before and after matching. Includes Love plots, density comparisons, and balance tables for propensity score matching diagnostics.
Usage
plot_balance_assessment(
matching_results,
plot_type = "love",
variables = NULL,
threshold = 0.1,
use_bw = FALSE,
show_sample_sizes = TRUE,
standardized = TRUE,
point_size = 3,
line_size = 0.8,
alpha = 0.7,
ncol = 2
)Arguments
- matching_results
List. Output from propensity_score_matching() function
- plot_type
Character. Type of balance plot: "love", "density", "histogram", "violin", "all". Default: "love"
- variables
Character vector. Variables to include in plots. Default: NULL (all variables)
- threshold
Numeric. Balance threshold line for Love plot. Default: 0.1
- use_bw
Logical. Use black and white theme for accessibility. Default: FALSE
- show_sample_sizes
Logical. Display sample sizes in plot titles. Default: TRUE
- standardized
Logical. Show standardized differences in Love plot. Default: TRUE
- point_size
Numeric. Size of points in Love plot. Default: 3
- line_size
Numeric. Size of connecting lines. Default: 0.8
- alpha
Numeric. Transparency for density plots. Default: 0.7
- ncol
Integer. Number of columns for faceted plots. Default: 2
Examples
if (FALSE) { # \dontrun{
# Assuming matching_results from propensity_score_matching()
balance_plot <- plot_balance_assessment(
matching_results = ps_match_results,
plot_type = "love",
threshold = 0.1
)
# Multiple balance plots
all_balance_plots <- plot_balance_assessment(
matching_results = ps_match_results,
plot_type = "all",
use_bw = TRUE
)
} # }