Print and Summarize Output

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 = 5L, ignore.s.weights = FALSE, weight.range = TRUE, ...)

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

# S3 method for class 'weightitMSM'
summary(
  object,
  top = 5L,
  ignore.s.weights = FALSE,
  weight.range = TRUE,
  which.time,
  ...
)

# S3 method for class 'summary.weightitMSM'
plot(x, binwidth = NULL, bins = NULL, which.time = 1L, ..., time)

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. Ignored when weight.range = FALSE.
ignore.s.weights: logical; 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.
weight.range: logical; whether to display statistics about the range of weights and the highest and lowest weights for each group. Default is TRUE.
...: 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.
which.time: for summary(), which models to summarize, given as a vector of positions in formula.list or of treatment or censoring variable names; omit it to summarize all of them. For plot(), which single model to display the distribution of weights for, given as one position or one name; default is the first. This mirrors the argument of the same name in cobalt::bal.tab() , except that a value matching no model is an error rather than a warning, since here it decides what is computed. Note that when censoring is modeled, the censoring models occupy positions of their own, so positions do not count treatment time points alone; naming the variable avoids having to count.
time: for plot(), the former name of which.time, which still works.

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 (\(\frac{1}{n}\sum w \log(w)\)) of the weights in each treatment group and overall, after dividing the weights by their mean in the corresponding group.
weight.mean (Mean of Weights): The mean of the weights in each treatment group and overall. Only included when the weights are stabilized.
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. When censoring is modeled (see .cens()), each censoring model gets an entry of its own, placed among the treatment entries in the order the models were fit and named for its censoring indicator. Every entry summarizes the same weights – the product across all the models – and differs only in the sample it summarizes them over: a treatment entry splits by treatment group, and a censoring entry covers the units still under observation when that model was fit, since the censored units have a weight of exactly 0 and are not part of the weighted sample.

plot() returns a ggplot object with a histogram displaying the distribution of the estimated weights. For a censoring model, only the weights of the units still under observation are displayed, matching what summary() reports. 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.

Examples

# See example at ?weightit or ?weightitMSM

Usage

Arguments

Value

See also

Examples