samur | R Documentation |
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.
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, ...)
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 |
matched.subset |
An integer vector representing the indexes of a subset of |
nsmp |
Number of stochastically matched subsets to generate. |
use.quantile |
Should numeric covariates be binned using quantiles ( |
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 |
... |
Arguments passed to/from other methods. |
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 |
Copy of data frame passed to the function. |
Mansour T.A. Sharabiani, Alireza S. Mahani
summary.samur
## 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)
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