samur {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 class 'samur'
print(x, ...)

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.

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.

Author(s)

Mansour T.A. Sharabiani, Alireza S. Mahani

See Also

summary.samur

Examples

## Not run: 
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)

## End(Not run)