Bootstrap Kolmogorov-Smirnov
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"), print.level=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 |
print.level |
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 |
nboots |
The number of bootstraps which were completed. |
Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, http://sekhon.berkeley.edu/.
Sekhon, Jasjeet S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization.” Journal of Statistical Software 42(7): 1-52. 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. 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.
Also see summary.ks.boot
,
qqstats
, balanceUV
, Match
,
GenMatch
, MatchBalance
,
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)
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