opt_nearfar: Finds optimal near-far match
In nearfar: Near-Far Matching

Description Usage Arguments Value Author(s) References Examples

Discovers optimal near-far matches using the partial F statistic (for continuous treatments) or partial deviance (for binary and treatments)

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opt_nearfar(dta, trt, covs, iv, trt.type = "cont", imp.var = NA,
tol.var = NA, adjust.IV = TRUE, sink.range = c(0, 0.5), cutp.range = NA,
max.time.seconds = 300)

`dta`	The name of the data frame on which matching was performed
`trt`	The name of the treatment variable, e.g., “educ”
`iv`	The name of the instrumental variable, e.g., iv="QOB"
`covs`	A vector of the names of the covariates to make “near”, e.g., covs=c("age", "sex", "race")
`trt.type`	Treatment variable type: “cont” for continuous, or “bin” for binary
`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
`adjust.IV`	if TRUE, include measured confounders in treatment~IV model that is optimized; if FALSE, exclude
`sink.range`	A two element vector of (min, max) for range of sinks over which to optimize in the near-far match; default (0, 0.5) such that maximally 50% of observations can be removed
`cutp.range`	a two element vector of (min, max) for range of cutpoints (how far apart the IV will become) over which to optimize in the near-far match; default is (one SD of IV, range of IV)
`max.time.seconds`	How long to let the optimization algorithm run; default is 300 seconds = 5 minutes

`n.calls`	Number of calls made to the objective function
`sink.range`	A two element vector of (min, max) for range of sinks over which to optimize in the near-far match; default (0, 0.5) such that maximally 50% of observations can be removed
`cutp.range`	a two element vector of (min, max) for range of cutpoints (how far apart the IV will become) over which to optimize in the near-far match; default is (one SD of IV, range of IV)
`pct.sink`	Optimal percent sinks
`cutp`	Optimal cutpoint
`maxF`	Highest value of partial F-statistic (continuous treatment) or residual deviance (binary treatment) found by simulated annealing optimizer
`match`	A two column matrix where the first column is the index of an “encouraged” individual and the second column is the index of the corresponding “discouraged” individual from the pair matching
`summ`	A table of mean variable values for both the “encouraged” and “discouraged” groups across all variables plus absolute standardized differences for each variable

Joseph Rigdon jrigdon@stanford.edu

Lu B, Greevy R, Xu X, Beck C (2011). Optimal nonbipartite matching and its statistical applications. The American Statistician, 65(1), 21-30.

Xiang Y, Gubian S, Suomela B, Hoeng J (2013). Generalized Simulated Annealing for Efficient Global Optimization: the GenSA Package for R. The R Journal, 5(1). URL http://journal.r-project.org/.

k = opt_nearfar(dta=mtcars, trt="drat", covs=c("cyl", "disp"),
    trt.type="cont", iv="carb", imp.var=NA, tol.var=NA, adjust.IV=TRUE,
    max.time.seconds=2)
summary(k)