Generates plots displaying distributional balance and overlap on covariates and propensity scores before and after matching and subclassification. For displaying balance solely on covariate standardized mean differences, see plot.summary.matchit. The plots here can be used to assess to what degree covariate and propensity score distributions are balanced and how weighting and discarding affect the distribution of propensity scores.
# S3 method for matchit
plot(x, type = "qq", interactive = TRUE,
which.xs = NULL, ...)# S3 method for matchit.subclass
plot(x, type = "qq", interactive = TRUE,
which.xs = NULL, subclass, ...)
a matchit object; the output of a call to matchit.
the type of plot to display. Options include "qq", "ecdf", "jitter", and "histogram". See Details. Default is "qq". Abbreviations allowed.
logical; whether the graphs should be displayed in an interactive way. Only applies for type = "qq", "ecdf", and "jitter". See Details.
with type = "qq" or "ecdf", for which covariate(s) plots should be displayed. Factor variables should be named by the original variable name rather than the names of individual dummy variables created after expansion with model.matrix.
with subclassification and type = "qq" or "ecdf", whether to display balance for individual subclasses, and, if so, for which ones. Can be TRUE (display plots for all subclasses), FALSE (display plots only in aggregate), or the indices (e.g., 1:6) of the specific subclasses for which to display balance. When unspecified, if interactive = TRUE, you will be asked for which subclasses plots are desired, and otherwise, plots will be displayed only in aggregate.
arguments passed to plot to control the appearance of the plot. Not all options are accepted.
A plot is displayed, and x is invisibly returned.
plot.matchit makes one of three different plots depending on the argument supplied to type.
With type = "qq", empirical quantile-quantile (eQQ) plots are created for each covariate before and after matching. The plots involve interpolating points in the smaller group based on the weighted quantiles of the other group. When points are approximately on the 45-degree line, the distributions in the treatment and control groups are approximately equal. Major deviations indicate departures from distributional balance. With variable with fewer than 5 unique values, points are jittered to more easily visualize counts. When interactive = TRUE, plots for three variables will be displayed at a time, and the prompt in the console allows you to move on to the next set of variables. When interactive = FALSE, multiple pages are plotted at the same time, but only the last few variables will be visible in the displayed plot. To see only a few specific variables at a time, use the which.xs argument to display eQQ plots for just those variables. If fewer than three (after expanding factors into their dummies), interactive is ignored.
With type = "ecdf", empirical cumulative density function (eCDF) plots are created for each covariate before and after matching. Two eCDF lines are produced in each plot: a gray one for control units and a black one for treated units. Each point on the lines corresponds to the proportion of units (or proportionate share of weights) less than or equal to the corresponding covariate value (on the x-axis). Deviations between the lines on the same plot indicates distributional imbalance between the treatment groups for the covariate. The eCDF and eQQ statistics in summary.matchit correspond to these plots: the eCDF max (also known as the Kolmogorov-Smirnov statistic) and mean are the largest and average vertical distance between the lines, and the eQQ max and mean are the largest and average horizontal distance between the lines.
With type = "jitter", a jitter plot is displayed for distance values before and after matching. This method requires a distance variable (e.g., a propensity score) to have been estimated or supplied in the call to matchit. The plot displays individuals values for matched and unmatched treatment and control units arranged horizontally by their propensity scores. Points are jitter so counts are easier to see. The size of the points increases when they receive higher weights. When interactive = TRUE, you can click on points in the graph to identify their rownames and indices to further probe extreme values, for example. With subclassification, vertical lines representing the subclass boundaries are overlay on the plots.
With type = "histogram", a histogram of distance values is displayed for the treatment and control groups before and after matching. This method requires a distance variable (e.g., a propensity score) to have been estimated or supplied in the call to matchit. With subclassification, vertical lines representing the subclass boundaries are overlay on the plots.
With all methods, sampling weights are incorporated into the weights if present.
summary.matchit for numerical summaries of balance, including those that rely on the eQQ and eCDF plots.
plot.summary.matchit for plotting standardized mean differences in a Love plot.
cobalt::bal.plot for displaying distributional balance in several other ways that are more easily customizable and produce ggplot2 objects. cobalt functions natively support matchit objects.
# NOT RUN {
data("lalonde")
m.out <- matchit(treat ~ age + educ + married +
race + re74, data = lalonde,
method = "nearest")
plot(m.out, type = "qq", interactive = FALSE,
which.xs = c("age", "educ", "married"))
plot(m.out, type = "histogram")
s.out <- matchit(treat ~ age + educ + married +
race + nodegree + re74 + re75,
data = lalonde, method = "subclass")
plot(s.out, type = "ecdf", interactive = FALSE,
which.xs = c("age", "educ", "married"),
subclass = 3)
plot(s.out, type = "jitter", interactive = FALSE)
# }
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