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"), 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 `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. |

`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.berkeley.edu/.

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. 2005. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.” Working Paper. http://sekhon.berkeley.edu/papers/GenMatch.pdf

Sekhon, Jasjeet Singh and Richard D. Grieve. 2011. "A Matching Method
For Improving Covariate Balance in Cost-Effectiveness Analyses."
*Health Economics*. forthcoming.

Sekhon, Jasjeet S. 2006. ``Alternative Balance Metrics for Bias Reduction in Matching Methods for Causal Inference.'' Working Paper. http://sekhon.berkeley.edu/papers/SekhonBalanceMetrics.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)