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Trims (i.e., winsorizes) large weights by setting all weights higher than
that at a given quantile to the weight at the quantile or to 0. This can be useful
in controlling extreme weights, which can reduce effective sample size by
enlarging the variability of the weights. Note that by default, no observations are
fully discarded when using trim(), which may differ from the some
uses of the word "trim" (see the drop argument below).
trim(x, ...)# S3 method for weightit
trim(x, at = 0, lower = FALSE, drop = FALSE, ...)
# S3 method for default
trim(x, at = 0, lower = FALSE, treat = NULL, drop = FALSE, ...)
If the input is a weightit object, the output will be a
weightit object with the weights replaced by the trimmed weights (or 0) and
will have an additional attribute, "trim", equal to the quantile of
trimming.
If the input is a numeric vector of weights, the output will be a numeric vector of the trimmed weights, again with the aforementioned attribute.
A weightit object or a vector of weights.
Not used.
numeric; either the quantile of the weights above which
weights are to be trimmed. A single number between .5 and 1, or the number
of weights to be trimmed (e.g., at = 3 for the top 3 weights to be
set to the 4th largest weight).
logical; whether also to trim at the lower quantile
(e.g., for at = .9, trimming at both .1 and .9, or for at = 3,
trimming the top and bottom 3 weights). Default is FALSE to only trim the higher weights.
logical; whether to set the weights of the trimmed units to 0 or not. Default is FALSE to retain all trimmed units. Setting to TRUE may change the original targeted estimand when not the ATT or ATC.
A vector of treatment status for each unit. This should always
be included when x is numeric, but you can get away with leaving it
out if the treatment is continuous or the estimand is the ATE for binary or
multi-category treatments.
trim() takes in a weightit object (the output of a call to
weightit() or weightitMSM()) or a numeric vector of weights and trims
(winsorizes) them to the specified quantile. All weights above that quantile
are set to the weight at that quantile unless drop = TRUE, in which case they are set to 0. If lower = TRUE, all weights
below 1 minus the quantile are trimmed. In
general, trimming weights can decrease balance but also decreases the
variability of the weights, improving precision at the potential expense of
unbiasedness (Cole & Hernán, 2008). See Lee, Lessler, and Stuart (2011) and
Thoemmes and Ong (2015) for discussions and simulation results of trimming
weights at various quantiles. Note that trimming weights can also change the
target population and therefore the estimand.
When using trim() on a numeric vector of weights, it is helpful to
include the treatment vector as well. The helps determine the type of
treatment and estimand, which are used to specify how trimming is performed.
In particular, if the estimand is determined to be the ATT or ATC, the
weights of the target (i.e., focal) group are ignored, since they should all
be equal to 1. Otherwise, if the estimand is the ATE or the treatment is
continuous, all weights are considered for trimming. In general, weights for
any group for which all the weights are the same will not be considered in
the trimming.
Cole, S. R., & Hernán, M. Á. (2008). Constructing Inverse Probability Weights for Marginal Structural Models. American Journal of Epidemiology, 168(6), 656–664.
Lee, B. K., Lessler, J., & Stuart, E. A. (2011). Weight Trimming and Propensity Score Weighting. PLoS ONE, 6(3), e18174.
Thoemmes, F., & Ong, A. D. (2016). A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models. Emerging Adulthood, 4(1), 40–59.
weightit(), weightitMSM()
library("cobalt")
data("lalonde", package = "cobalt")
(W <- weightit(treat ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "glm", estimand = "ATT"))
summary(W)
#Trimming the top and bottom 5 weights
trim(W, at = 5, lower = TRUE)
#Trimming at 90th percentile
(W.trim <- trim(W, at = .9))
summary(W.trim)
#Note that only the control weights were trimmed
#Trimming a numeric vector of weights
all.equal(trim(W$weights, at = .9, treat = lalonde$treat),
W.trim$weights)
#Dropping trimmed units
(W.trim <- trim(W, at = .9, drop = TRUE))
summary(W.trim)
#Note that we now have zeros in the control group
#Using made up data and as.weightit()
treat <- rbinom(500, 1, .3)
weights <- rchisq(500, df = 2)
W <- as.weightit(weights, treat = treat,
estimand = "ATE")
summary(W)
summary(trim(W, at = .95))
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