R: Bootstrap Kolmogorov-Smirnov

ks.boot {Matching} | R Documentation

## 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.

### Author(s)

Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, http://sekhon.berkeley.edu/.

### References

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.

### See Also

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

### Examples

    #
    # 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)

[Package Matching version 4.10-2 Index]