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).
Usage
trim(x, ...)
# S3 method for class 'weightit'
trim(x, at = 0, lower = FALSE, drop = FALSE, ...)
# Default S3 method
trim(x, at = 0, lower = FALSE, treat = NULL, drop = FALSE, ...)Arguments
- x
A
weightitobject or a vector of weights.- ...
Not used.
- at
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 = 3for the top 3 weights to be set to the 4th largest weight).- lower
logical; whether also to trim at the lower quantile (e.g., forat = .9, trimming at both .1 and .9, or forat = 3, trimming the top and bottom 3 weights). Default isFALSEto only trim the higher weights.- drop
logical; whether to set the weights of the trimmed units to 0 or not. Default isFALSEto retain all trimmed units. Setting toTRUEmay change the original targeted estimand when not the ATT or ATC.- treat
A vector of treatment status for each unit. This should always be included when
xis 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.
Value
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.
Details
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.
References
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.
Examples
library("cobalt")
data("lalonde", package = "cobalt")
(W <- weightit(treat ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "glm", estimand = "ATT"))
#> A weightit object
#> - method: "glm" (propensity score weighting with GLM)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATT (focal: 1)
#> - covariates: age, educ, married, nodegree, re74
summary(W)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0000 || 1.0000
#> control 0.0222 |---------------------------| 2.0438
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 5 4 3 2 1
#> treated 1 1 1 1 1
#> 411 595 269 409 296
#> control 1.3303 1.4365 1.5005 1.6369 2.0438
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.000 0.000 0.00 0
#> control 0.823 0.701 0.33 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185
#> Weighted 255.99 185
#Trimming the top and bottom 5 weights
trim(W, at = 5, lower = TRUE)
#> Trimming the top and bottom 5 weights where treat is not 1.
#> A weightit object
#> - method: "glm" (propensity score weighting with GLM)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATT (focal: 1)
#> - covariates: age, educ, married, nodegree, re74
#> - weights trimmed at the top and bottom 5
#Trimming at 90th percentile
(W.trim <- trim(W, at = .9))
#> Trimming weights where treat is not 1 to 90%.
#> A weightit object
#> - method: "glm" (propensity score weighting with GLM)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATT (focal: 1)
#> - covariates: age, educ, married, nodegree, re74
#> - weights trimmed at 90%
summary(W.trim)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0000 || 1.0000
#> control 0.0222 |-------------------------| 0.9407
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 5 4 3 2 1
#> treated 1 1 1 1 1
#> 303 296 285 269 264
#> control 0.9407 0.9407 0.9407 0.9407 0.9407
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.000 0.000 0.000 0
#> control 0.766 0.682 0.303 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185
#> Weighted 270.58 185
#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)
#> Trimming weights where treat is not 1 to 90%.
#> [1] TRUE
#Dropping trimmed units
(W.trim <- trim(W, at = .9, drop = TRUE))
#> Setting weights beyond 90% where treat is not 1 to 0.
#> A weightit object
#> - method: "glm" (propensity score weighting with GLM)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATT (focal: 1)
#> - covariates: age, educ, married, nodegree, re74
#> - weights trimmed at 90%
summary(W.trim)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0000 || 1.0000
#> control 0.0222 |-------------------------| 0.9407
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 5 4 3 2 1
#> treated 1 1 1 1 1
#> 467 466 373 369 356
#> control 0.9407 0.9407 0.9407 0.9407 0.9407
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.000 0.000 0.000 0
#> control 0.881 0.757 0.303 40
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185
#> Weighted 241.72 185
#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 of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 0.0141 |-------------------------| 10.6020
#> control 0.0069 |---------------------------| 11.2619
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 238 374 324 24 275
#> treated 7.3089 7.6362 7.9896 9.2475 10.602
#> 171 299 75 154 346
#> control 8.5277 8.5762 8.6373 9.8308 11.2619
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.957 0.74 0.396 0
#> control 0.924 0.71 0.381 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 345. 155.
#> Weighted 186.44 81.16
summary(trim(W, at = .95))
#> Trimming weights to 95%.
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 0.0141 |---------------------------| 6.1429
#> control 0.0069 |---------------------------| 6.1429
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 189 177 88 24 5
#> treated 6.1429 6.1429 6.1429 6.1429 6.1429
#> 154 136 135 95 75
#> control 6.1429 6.1429 6.1429 6.1429 6.1429
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.875 0.714 0.357 0
#> control 0.844 0.690 0.346 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 345. 155.
#> Weighted 201.82 88.05