`summary()`

generates a summary of the `weightit`

or
`weightitMSM`

object to evaluate the properties of the estimated
weights. `plot()`

plots the distribution of the weights.

## Usage

```
# S3 method for weightit
summary(object, top = 5, ignore.s.weights = FALSE, ...)
# S3 method for summary.weightit
plot(x, binwidth = NULL, bins = NULL, ...)
# S3 method for weightitMSM
summary(object, top = 5, ignore.s.weights = FALSE, ...)
# S3 method for summary.weightitMSM
plot(x, binwidth = NULL, bins = NULL, time = 1, ...)
```

## Arguments

- object
a

`weightit`

or`weightitMSM`

object; the output of a call to`weightit()`

or`weightitMSM()`

.- 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.- ...
For

`plot()`

, additional arguments passed to`graphics::hist()`

to determine the number of bins, though`ggplot2::geom_histogram()`

is actually used to create the plot.- x
a

`summary.weightit`

or`summary.weightitMSM`

object; the output of a call to`summary.weightit()`

or`summary.weightitMSM()`

.- binwidth, bins
arguments passed to

`ggplot2::geom_histogram()`

to control the size and/or number of bins.- time
`numeric`

; the time point for which to display the distribution of weights. Default is to plot the distribution for the first time points.

## Value

For point treatments (i.e., `weightit`

objects), a `summary.weightit`

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 (Coef of Var)
The coefficient of variation (standard deviation divided by mean) of the weights in each treatment group and overall.

- scaled.mad (MAD)
The mean absolute deviation of the weights in each treatment group and overall divided by the mean of the weights in the corresponding group.

- negative entropy (Entropy)
The negative entropy (\(\sum w log(w)\)) of the weights in each treatment group and overall divided by the mean of the weights in the corresponding group.

- num.zeros
The number of weights equal to zero.

- effective.sample.size
The effective sample size for each treatment group before and after weighting. See

`ESS()`

.

For longitudinal treatments (i.e., `weightitMSM`

objects), a list of
the above elements for each treatment period.

`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.