Matching (version 4.9-2)

ks.boot: Bootstrap Kolmogorov-Smirnov

Description

This function executes a bootstrap version of the univariate Kolmogorov-Smirnov test which provides correct coverage even when the distributions being compared are not entirely continuous. Ties are allowed with this test unlike the traditional Kolmogorov-Smirnov test.

Usage

ks.boot(Tr, Co, nboots=1000, alternative = c("two.sided","less","greater"),
        print.level=0)

Arguments

Tr

A vector containing the treatment observations.

Co

A vector containing the control observations.

nboots

The number of bootstraps to be performed. These are, in fact, really Monte Carlo simulations which are preformed in order to determine the proper p-value from the empiric.

alternative

indicates the alternative hypothesis and must be one of '"two.sided"' (default), '"less"', or '"greater"'. You can specify just the initial letter. See ks.test for details.

print.level

If this is greater than 1, then the simulation count is printed out while the simulations are being done.

Value

ks.boot.pvalue

The bootstrap p-value of the Kolmogorov-Smirnov test for the hypothesis that the probability densities for both the treated and control groups are the same.

ks

Return object from ks.test.

nboots

The number of bootstraps which were completed.

References

Sekhon, Jasjeet S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization.'' Journal of Statistical Software 42(7): 1-52. http://www.jstatsoft.org/v42/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. http://sekhon.berkeley.edu/papers/GenMatch.pdf

Abadie, Alberto. 2002. ``Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models.'' Journal of the American Statistical Association, 97:457 (March) 284-292.

See Also

Also see summary.ks.boot, qqstats, balanceUV, Match, GenMatch, MatchBalance, GerberGreenImai, lalonde

Examples

# NOT RUN {
#
# Replication of Dehejia and Wahba psid3 model
#
# Dehejia, Rajeev and Sadek Wahba. 1999.``Causal Effects in
# Non-Experimental Studies: Re-Evaluating the Evaluation of Training
# Programs.''Journal of the American Statistical Association 94 (448):
# 1053-1062.
#
data(lalonde)

#
# Estimate the propensity model
#
glm1  <- glm(treat~age + I(age^2) + educ + I(educ^2) + black +
             hisp + married + nodegr + re74  + I(re74^2) + re75 + I(re75^2) +
             u74 + u75, family=binomial, data=lalonde)


#
#save data objects
#
X  <- glm1$fitted
Y  <- lalonde$re78
Tr  <- lalonde$treat

#
# one-to-one matching with replacement (the "M=1" option).
# Estimating the treatment effect on the treated (the "estimand" option which defaults to 0).
#
rr  <- Match(Y=Y,Tr=Tr,X=X,M=1);
summary(rr)

#
# Do we have balance on 1975 income after matching?
#
ks  <- ks.boot(lalonde$re75[rr$index.treated], lalonde$re75[rr$index.control], nboots=500)
summary(ks)
# }