This function plots the absolute difference in standardized means before and after
weighting. To access more sophisticated graphics for assessing covariate balance,
consider using Noah Greifer's cobalt package.
# S3 method for CBPS
plot(x, covars = NULL, silent = TRUE, boxplot = FALSE, ...)For binary and multi-valued treatments, plots the absolute difference in standardized means by contrast for all covariates before and after weighting. This quantity for a single covariate and a given pair of treatment conditions is given by \(\frac{\sum_{i=1}^{n} w_i * (T_i == 1) * X_i}{\sum_{i=1}^{n} (T_i == 1) * w_i} - \frac{\sum_{i=1}^{n} w_i * (T_i == 0) * X_i}{\sum_{i=1}^{n} (T_i == 0) * w_i}\). For continuous treatments, plots the weighted absolute Pearson correlation between the treatment and each covariate. See https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#Weighted_correlation_coefficient.
an object of class “CBPS” or “npCBPS”, usually, a
result of a call to CBPS or npCBPS.
Indices of the covariates to be plotted (excluding the
intercept). For example, if only the first two covariates from
balance are desired, set covars to 1:2. The default is
NULL, which plots all covariates.
If set to FALSE, returns the imbalances used to
construct the plot. Default is TRUE, which returns nothing.
If set to TRUE, returns a boxplot summarizing the
imbalance on the covariates instead of a point for each covariate. Useful
if there are many covariates.
Additional arguments to be passed to plot.
Christian Fong, Marc Ratkovic, and Kosuke Imai.
The "Before Weighting" plot gives the balance before weighting, and the "After Weighting" plot gives the balance after weighting.
### @aliases plot.CBPS plot.npCBPS
CBPS, plot