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. nobs() extracts the number of observations.
Usage
# S3 method for class 'weightit'
summary(object, top = 5, ignore.s.weights = FALSE, ...)
# 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'
plot(x, binwidth = NULL, bins = NULL, time = 1, ...)Arguments
- object
a
weightitorweightitMSMobject; the output of a call toweightit()orweightitMSM().- 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 tographics::hist()to determine the number of bins, thoughggplot2::geom_histogram()is actually used to create the plot.- x
a
summary.weightitorsummary.weightitMSMobject; the output of a call tosummary.weightit()orsummary.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), summary() returns 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), summary() returns 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.
nobs() returns a single number. Note that even units with weights or s.weights of 0 are included.