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.
# S3 method for weightit
summary(object, top = 5,
ignore.s.weights = FALSE, ...)# S3 method for summary.weightit
print(x, ...)
# 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
print(x, ...)
a weightit
or weightitMSM
object; the output of a call to weightit()
or weightitMSM()
.
how many of the largest and smallest weights to display. Default is 5.
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.
arguments passed to geom_histogram()
to control the size and/or number of bins.
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.
For point treatments (i.e., weightit
objects), a summary.weightit
object with the following elements:
The range (minimum and maximum) weight for each treatment group.
The units with the greatest weights in each treatment group; how many are included is determined by top
.
The coefficient of variation (standard deviation divided by mean) of the weights in each treatment group and overall.
The mean absolute deviation of the weights in each treatment group and overall divided by the mean of the weights in the corresponding group.
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.
The number of weights equal to zero.
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.
# NOT RUN {
# See example at ?weightit or ?weightitMSM
# }
Run the code above in your browser using DataLab