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. See method_optweight for details.