WeightIt: summary.weightit – R documentation

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summary.weightit

Print and Summarize Output

Description

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 class 'weightit'
summary(object, top = 5,
        ignore.s.weights = FALSE, ...)

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

## S3 method for class 'summary.weightit'
plot(x, binwidth = NULL, bins = NULL, ...)

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

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

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.
`binwidth, bins`	arguments passed to `geom_histogram()` to control the size and/or number of bins.
`x`	a `summary.weightit` or `summary.weightitMSM` object; the output of a call to `summary.weightit()` or `summary.weightitMSM()`.
`...`	for `print()`, arguments passed to `print()`. 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., 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 (∑ 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.

Author(s)

Noah Greifer

Examples

# See example at ?weightit or ?weightitMSM

WeightIt

Weighting for Covariate Balance in Observational Studies

v0.12.0

GPL (>= 2)

Authors

Noah Greifer [aut, cre] (<https://orcid.org/0000-0003-3067-7154>)

Initial release