samur: Stochastic Augmentation of Matched Datasets Using Restriction Methods

Description

This function generates multiple subsets of the data in which the distribution of covariates is balanced across treatment groups. It works by binning the output of a base matching algorithm into a multidimensional histogram, and drawing - without replacement - from the full data set according to the histogram. This leads to higher data coverage across multiple matched subsets without duplication of cases within each subset.

Usage

samur(
  formula, data
  , matched.subset = 1:nrow(data)
  , nsmp = 100
  , use.quantile = TRUE, breaks = 10
  , replace = length(unique(matched.subset)) < length(matched.subset)
  )
# S3 method for samur
print(x, ...)

Value

An object of class samur, a matrix of size length(matched.subset) by nsmp, where each column is a matched subset wihtout case duplication. It also has the following attributes:

call: Copy of function call.
formula: Formula passed to the function.
mdg: Multi-dimensional grid used for binning the matched data subsets.
mdh: Multi-dimensional histogram resulting frm binning data[matched.subset, ] according to the grid specified in mdg.
data: Copy of data frame passed to the function.

Arguments

formula: Formula expression used to describe the treatment variable (lhs) and covariates used during matching (rhs).
data: Data frame containing the treatment variables and matched covariates as specified in the formula.
matched.subset: An integer vector representing the indexes of a subset of data that is the output of a base matching algorithm. It cannot contain duplicate values.
nsmp: Number of stochastically matched subsets to generate.
use.quantile: Should numeric covariates be binned using quantiles (TRUE) or not.
breaks: number of breaks to use in binning numeric covariates.
replace: Boolean flag indicating whether or not to perform sampling with replacement.
x: An object of class samur, typically the output of function samur.
...: Arguments passed to/from other methods.

Author

Mansour T.A. Sharabiani, Alireza S. Mahani

Examples

Run this code

if (FALSE) {
library(SAMUR)
library(Matching)
data(lalonde)
myformula <- treat ~ age + educ
myglm <- glm(myformula, lalonde, family="binomial")
X <- myglm$fitted.values
# using M=1 and replace=F to ensure no duplication
bimatch <- Match(Tr = lalonde$treat, X = myglm$fitted.values
  , M = 1, replace = F, caliper = 0.25)
idx <- c(bimatch$index.control, bimatch$index.treated)
my.samur <- samur(formula = myformula, data = lalonde
  , matched.subset = idx, nsmp = 100
  , breaks = 10, use.quantile = TRUE)
summary(my.samur, nboots = 500)
}