balanceUV  R Documentation 
This function provides a number of univariate balance metrics.
Generally, users should call MatchBalance
and not this function
directly.
balanceUV(Tr, Co, weights = rep(1, length(Co)), exact = FALSE, ks=FALSE,
nboots = 1000, paired=TRUE, match=FALSE,
weights.Tr=rep(1,length(Tr)), weights.Co=rep(1,length(Co)),
estimand="ATT")
Tr 
A vector containing the treatment observations. 
Co 
A vector containing the control observations. 
weights 
A vector containing the observation specific weights. Only use this option when the treatment and control observations are paired (as they are after matching). 
exact 
A logical flag indicating if the exact Wilcoxon test
should be used instead of the test with a correction. See

ks 
A logical flag for if the univariate bootstrap
KolmogorovSmirnov (KS) test should be calculated. If the ks option
is set to true, the univariate KS test is calculated for all
nondichotomous variables. The bootstrap KS test is consistent even
for noncontinuous variables. See 
nboots 
The number of bootstrap samples to be run for the

paired 
A flag for if the paired 
match 
A flag for if the 
weights.Tr 
A vector of weights for the treated observations. 
weights.Co 
A vector of weights for the control observations. 
estimand 
This determines if the standardized mean difference
returned by the 
sdiff 
This is the standardized difference between the treated
and control units multiplied by 100. That is, 100 times the mean
difference between treatment and control units divided by the standard
deviation of the treatment
observations alone if the estimand is either 
sdiff.pooled 
This is the standardized difference between the treated and control units multiplied by 100 using the pooled variance. That is, 100 times the mean difference between treatment and control units divided by the pooled standard deviation as in Rosenbaum and Rubin (1985). 
mean.Tr 
The mean of the treatment group. 
mean.Co 
The mean of the control group. 
var.Tr 
The variance of the treatment group. 
var.Co 
The variance of the control group. 
p.value 
The pvalue from the twosided weighted 
var.ratio 
var.Tr/var.Co. 
ks 
The object returned by 
tt 
The object returned by twosided weighted

qqsummary 
The return object from a call to

qqsummary.raw 
The return object from a call to

Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, https://www.jsekhon.com.
Sekhon, Jasjeet S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization.” Journal of Statistical Software 42(7): 152. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v042.i07")}
Diamond, Alexis and Jasjeet S. Sekhon. 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.” Review of Economics and Statistics. 95 (3): 932–945. https://www.jsekhon.com
Rosenbaum, Paul R. and Donald B. Rubin. 1985. “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score.” The American Statistician 39:1 3338.
Hollander, Myles and Douglas A. Wolfe. 1973. Nonparametric statistical inference. New York: John Wiley & Sons.
Also see summary.balanceUV
, qqstats
ks.boot
, Match
, GenMatch
,
MatchBalance
,
GerberGreenImai
, lalonde
data(lalonde)
attach(lalonde)
foo < balanceUV(re75[treat==1],re75[treat!=1])
summary(foo)
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