nbpMatching-package: Nonbipartite Matching

In nbpMatching: Functions for Optimal Non-Bipartite Matching

Description

This package will take an input distance matrix and generate the set of pairwise matches that minimizes the sum of distances between the pairs by running nonbimatch.

Details

The most current documentation is available at http://biostat.mc.vanderbilt.edu/wiki/Main/MatchedRandomization.

Author(s)

Bo Lu, Robert Greevy, Cole Beck

Maintainer: Cole Beck [e-mail]

References

Lu B, Greevy R, Xu X, Beck C. Optimal Nonbipartite Matching and its Statistical Applications. The American Statistician. Vol. 65, no. 1. : 21-30. 2011.

Greevy RA Jr, Grijalva CG, Roumie CL, Beck C, Hung AM, Murff HJ, Liu X, Griffin MR. Reweighted Mahalanobis distance matching for cluster-randomized trials with missing data. Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:148-54. doi: 10.1002/pds.3260.

Examples

# create a covariate matrix
df <- data.frame(id=LETTERS[1:25], val1=rnorm(25), val2=rnorm(25))
# create distances
df.dist <- gendistance(df, idcol=1)
# create distancematrix object
df.mdm <- distancematrix(df.dist)
# create matches
df.match <- nonbimatch(df.mdm)
# review quality of matches
df.qom <- qom(df.dist$cov, df.match$matches)

# some helper functions are available
# runner -- start with the covariate, run through the entire process
df.1 <- runner(df, idcol=1)
# full.qom -- start with the covariate, generate a full quality of match report
df.2 <- full.qom(df)

## Not run: 
try a large matrix
nonbimatch(distancematrix(as.matrix(dist(sample(1:10^8, 5000, replace=TRUE)))))

## End(Not run)

nbpMatching documentation built on May 2, 2019, 5:23 p.m.