matches | R Documentation |
opt_nearfar
to discover optimal near-far matches.
Given values of percent sinks and cutpoint, this function will find the corresponding near-far match
matches(dta, covs, iv = NA, imp.var = NA, tol.var = NA, sinks = 0,
cutpoint = NA)
dta |
The name of the data frame on which to do the matching |
covs |
A vector of the names of the covariates to make “near”, e.g., covs=c("age", "sex", "race") |
iv |
The name of the instrumental variable, e.g., iv="QOB" |
imp.var |
A list of (up to 5) named variables to prioritize in the “near” matching |
tol.var |
A list of (up to 5) tolerances attached to the prioritized variables where 0 is highest penalty for mismatch |
sinks |
Percentage of the data to match to sinks (and thus remove) if desired; default is 0 |
cutpoint |
Value below which individuals are too similar on iv; increase to make individuals more “far” in match |
Default settings yield a "near" match on only observed confounders in X; add IV, sinks, and cutpoint to get near-far match.
A two-column matrix of row indices of paired matches
Joseph Rigdon jrigdon@wakehealth.edu
Lu B, Greevy R, Xu X, Beck C (2011). Optimal nonbipartite matching and its statistical applications. The American Statistician, 65(1), 21-30.
opt_nearfar
k2 = matches(dta=mtcars, covs=c("cyl", "disp"), sinks=0.2, iv="carb",
cutpoint=2, imp.var=c("cyl"), tol.var=0.03)
k2[1:5, ]
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