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Trim IPTW balancing weights, if needed

Source: R/trimWeights.R
trimWeights.Rd

Trims IPTW balancing weights with heavy right tails by populating all weight values above a given quantile with the weight value of that quantile.

Usage

trimWeights(weights, at = 0, lower = FALSE, verbose = FALSE, save.out = FALSE)

Arguments

weights

list of IPTW weights output from createWeights()

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.

verbose

(optional) TRUE or FALSE indicator for printing output to console. default is FALSE.

save.out

(optional) Either logical or a character string. If TRUE, it will output the result to a default file name within home_dir set in initMSM(). You can load the data with x <- readRDS(file). To use a non-default file name, specify a character string with the file name. It will save relative to home_dir. There might be naming conflicts where two objects get saved to the same file. In these cases, users should specify a custom name. default is FALSE.

Value

a list containing WeightIt::weightitMSM() output. It is the length of the number of datasets (1 for a data.frame or the number of imputed datasets).

See also

WeightIt::trim(), https://search.r-project.org/CRAN/refmans/WeightIt/html/trim.html which this function wraps

Examples

library(devMSMs)
data <- data.frame(
  ID = 1:50,
  A.1 = rnorm(n = 50),
  A.2 = rnorm(n = 50),
  A.3 = rnorm(n = 50),
  B.1 = rnorm(n = 50),
  B.2 = rnorm(n = 50),
  B.3 = rnorm(n = 50),
  C = rnorm(n = 50),
  D.3 = rnorm(n = 50)
)
obj <- initMSM(
  data,
  exposure = c("A.1", "A.2", "A.3"),
  ti_conf = c("C"),
  tv_conf = c("B.1", "B.2", "B.3", "D.3")
)
f <- createFormulas(obj, type = "short")

w <- createWeights(data = data, formulas = f)
tw <- trimWeights(w, at = 0.975)
print(tw)
#> 
#> For the `glm` weighting method, after trimming at 97.5th quantile, the median weight value is 0.97 (SD = 0.32; range = 0.43-2).
plot(tw)


trimWeights(w, at = 0.975, lower = TRUE)
#> 
#> For the `glm` weighting method, after trimming between 2.5th and 97.5th quantiles, the median weight value is 0.97 (SD = 0.32; range = 0.43-2).