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
orweightitMSM
object; 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.weightit
orsummary.weightitMSM
object; 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), 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.