plot.weightit() plots information about the weights depending on how they were estimated. Currently, only weighting using method = "gbm" or "optweight" is supported. To plot the distribution of weights, see plot.summary.weightit().
Usage
# S3 method for class 'weightit'
plot(x, ...)Arguments
- x
a
weightitobject; the output of a call toweightit().- ...
Unused.
Details
method = "gbm"
After weighting with generalized boosted modeling, plot() displays the results of the tuning process used to find the optimal number of trees (and tuning parameter values, if modified) that are used in the final weights. The plot produced has the number of trees on the x-axis and the value of the criterion on the y axis with a diamond at the optimal point. When multiple parameters are selected by tuning, a separate line is displayed on the plot for each combination of tuning parameters. When by is used in the call to weightit(), the plot is faceted by the by variable. See method_gbm for more information on selecting tuning parameters.
method = "optweight"
After estimating stable balancing weights, plot() displays the values of the dual variables for each balance constraint in a bar graph. Large values of the dual variables indicate the covariates for which the balance constraint is causing increases in the variability of the weights, i.e., the covariates for which relaxing the imbalance tolerance would yield the greatest gains in effective sample size. For continuous treatments, the dual variables are split into those for the target (i.e., ensuring the mean of each covariate after weighting is equal to its unweighted mean) and those for balance (i.e., ensuring the treatment-covariate correlations are no larger than the imbalance tolerance). This is essentially a wrapper for optweightplot.optweight.