summary.optweight: Summarize, print, and plot information about estimated...

In optweight: Targeted Stable Balancing Weights Using Optimization

Description

These functions summarize the weights resulting from a call to optweight or optweight.svy. summary produces summary statistics on the distribution of weights, including their range and variability, and the effective sample size of the weighted sample (computing using the formula in McCaffrey, Rudgeway, & Morral, 2004). plot creates a histogram of the weights.

Usage

## S3 method for class 'optweight'
summary(object, top = 5, ignore.s.weights = FALSE, ...)

## S3 method for class 'optweightMSM'
summary(object, top = 5, ignore.s.weights = FALSE, ...)

## S3 method for class 'optweight.svy'
summary(object, top = 5, ignore.s.weights = FALSE, ...)

## S3 method for class 'summary.optweight'
print(x, ...)

## S3 method for class 'summary.optweightMSM'
print(x, ...)

## S3 method for class 'summary.optweight.svy'
print(x, ...)

## S3 method for class 'summary.optweight'
plot(x, ...)

Arguments

object	An optweight, optweightMSM, or optweight.svy object; the output of a call to optweight or optweight.svy.
top	How many of the largest and smallest weights to display. Default is 5.
ignore.s.weights	Whether or not to ignore sampling weights when computing the weight summary. If FALSE, the default, the estimated weights will be multiplied by the sampling weights (if any) before values are computed.
x	A summary.optweight, summary.optweightMSM, or summary.optweight.svy object; the output of a call to summary.optweight, summary.optweightMSM, or summary.optweight.svy.
...	Additional arguments. For plot, additional arguments passed to hist to determine the number of bins, though geom_histogram from ggplot2 is actually used to create the plot.

Value

For point treatments (i.e., optweight objects), summary returns a summary.optweight object with the following elements:

weight.range	The range (minimum and maximum) weight for each treatment group.
weight.top	The units with the greatest weights in each treatment group; how many are included is determined by top.
coef.of.var	The coefficient of variation (standard deviation divided by mean) of the weights in each treatment group and overall. When no sampling weights are used, this is simply the standard deviation of the weights.
mean.abs.dev	The mean absolute deviation of the weights in each treatment group and overall.
effective.sample.size	The effective sample size for each treatment group before and after weighting.

For longitudinal treatments (i.e., optweightMSM objects), a list of the above elements for each treatment period.

For optweight.svy objects, a list of the above elements but with no treatment group divisions.

plot returns a ggplot object with a histogram displaying the distribution of the estimated weights. If the estimand is the ATT or ATC, only the weights for the non-focal group(s) will be displayed (since the weights for the focal group are all 1). A dotted line is displayed at the mean of the weights (usually 1).

Author(s)

Noah Greifer

References

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies. Psychological Methods, 9(4), 403–425. doi: 10.1037/1082-989X.9.4.403

See Also

plot.optweight for plotting the values of the dual variables.

Examples

library("cobalt")
data("lalonde", package = "cobalt")

#Balancing covariates between treatment groups (binary)
(ow1 <- optweight(treat ~ age + educ + married +
                nodegree + re74, data = lalonde,
                tols = .001,
                estimand = "ATT"))

(s <- summary(ow1))

plot(s, breaks = 12)

optweight documentation built on Sept. 16, 2019, 5:02 p.m.