| ks.boot {Matching} | R Documentation |
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.
ks.boot(Tr, Co, nboots=1000, alternative = c("two.sided", "less", "greater"), verbose=0)
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. |
verbose |
If this is greater than 1, then the simulation count is printed out while the simulations are being done. |
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. |
Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, http://sekhon.polisci.berkeley.edu/.
Sekhon, Jasjeet S. 2004. ``Balance Tests for Matching Estimators.'' Working Paper.
Diamond, Alexis and Jasjeet S. Sekhon. 2005. ``Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.'' Working Paper. http://sekhon.polisci.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.
Also see summary.ks.boot,
balanceUV, Match,
GenMatch,
MatchBalance,
balanceMV, GerberGreenImai, lalonde
#
# 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)