MatchThem (version 1.2.1)

trim: Trim Weights

Description

Trims (i.e., truncates) large weights by setting all weights higher than that at a given quantile to the weight at the quantile. This can be useful in controlling extreme weights, which can reduce effective sample size by enlarging the variability of the weights.

Usage

# S3 method for wimids
trim(x, at = 0, lower = FALSE, ...)

Value

An object from the wimids class, identical to the original object except with trim() applied to each of the weightit objects in the models component.

Arguments

x

A wimids object; the output of a call to weightthem().

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 = 3 for the top 3 weights to be set to the 4th largest weight).

lower

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.

...

Ignored.

Author

Noah Greifer

Details

trim.wimids() works by calling WeightIt::trim() on each weightit object stored in the models component of the wimids object. Because trim() itself is not exported from MatchThem, it must be called using WeightIt::trim() or by attaching WeightIt (i.e., running library(WeightIt)) before use.

See Also

WeightIt::trim()

Examples

#Loading the dataset
data(osteoarthritis)

#Multiply imputing the missing values
imputed.datasets <- mice::mice(osteoarthritis, m = 5)

#Estimating weights of observations in the multiply imputed datasets
weighted.datasets <- weightthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
                                imputed.datasets,
                                approach = 'within',
                                method = 'glm',
                                estimand = "ATE")

#Trimming the top 10% of weights in each dataset
#to the 90th percentile
trimmed.datasets <- trim(weighted.datasets, at = 0.9)