qqstats {Matching} | R Documentation |

This function calculates a set of summary statistics for the QQ
plot of two samples of data. The summaries are useful for determining
if the two samples are from the same distribution. If
`standardize==TRUE`

, the empirical CDF is used instead of the
empirical-QQ plot. The later retains the scale of the variable.

qqstats(x, y, standardize=TRUE, summary.func)

`x` |
The first sample. |

`y` |
The second sample. |

`standardize` |
A logical flag for whether the statistics should be standardized by the empirical cumulative distribution functions of the two samples. |

`summary.func` |
A user provided function to summarize the
difference between the two distributions. The function should
expect a vector of the differences as an argument and return summary
statistic. For example, the `quantile` function is a
legal function to pass in. |

`meandiff` |
The mean difference between the QQ plots of the two samples. |

`mediandiff` |
The median difference between the QQ plots of the two samples. |

`maxdiff` |
The maximum difference between the QQ plots of the two samples. |

`summarydiff` |
If the user provides a `summary.func` , the
user requested summary difference is returned. |

`summary.func` |
If the user provides a `summary.func` , the
function is returned. |

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

Also see `ks.boot`

,
`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$treat # # one-to-one matching with replacement (the "M=1" option). # Estimating the treatment effect on the treated (the "estimand" option which defaults to 0). # rr <- Match(Y=Y,Tr=Tr,X=X,M=1); summary(rr) # # Do we have balance on 1975 income after matching? # qqout <- qqstats(lalonde$re75[rr$index.treated], lalonde$re75[rr$index.control]) print(qqout)