balanceUV {Matching} | R Documentation |

This function provides a number of univariate balance metrics.
Generally, users should call `MatchBalance`

and not this function
directly.

balanceUV(Tr, Co, weights = rep(1, length(Co)), exact = FALSE, ks=FALSE, nboots = 1000, paired=TRUE, match=FALSE, weights.Tr=rep(1,length(Tr)), weights.Co=rep(1,length(Co)), estimand="ATT")

`Tr` |
A vector containing the treatment observations. |

`Co` |
A vector containing the control observations. |

`weights` |
A vector containing the observation specific weights. Only use this option when the treatment and control observations are paired (as they are after matching). |

`exact` |
A logical flag indicating if the exact Wilcoxon test
should be used instead of the test with a correction. See
`wilcox.test` for details. |

`ks` |
A logical flag for if the univariate bootstrap
Kolmogorov-Smirnov (KS) test should be calculated. If the ks option
is set to true, the univariate KS test is calculated for all
non-dichotomous variables. The bootstrap KS test is consistent even
for non-continuous variables. See `ks.boot` for more
details. |

`nboots` |
The number of bootstrap samples to be run for the
`ks` test. If zero, no bootstraps are done. Bootstrapping is
highly recommended because the bootstrapped Kolmogorov-Smirnov test
only provides correct coverage even for non-continuous covariates. At
least 500 `nboots` (preferably 1000) are
recommended for publication quality p-values. |

`paired` |
A flag for if the paired `t.test` should be used. |

`match` |
A flag for if the `Tr` and `Co` objects are the result
of a call to `Match` . |

`weights.Tr` |
A vector of weights for the treated observations. |

`weights.Co` |
A vector of weights for the control observations. |

`estimand` |
This determines if the standardized mean difference
returned by the `sdiff` object is standardized by the variance of
the treatment observations (which is done if the estimand is either
"ATE" or "ATT") or by the variance of the control observations (which
is done if the estimand is "ATC"). |

`sdiff` |
This is the standardized difference between the treated
and control units multiplied by 100. That is, 100 times the mean
difference between treatment and control units divided by the standard
deviation of the treatment
observations alone if the estimand is either `ATT` or
`ATE` . The variance of the control observations are used if
the estimand is `ATC` . |

`sdiff.pooled` |
This is the standardized difference between the treated and control units multiplied by 100 using the pooled variance. That is, 100 times the mean difference between treatment and control units divided by the pooled standard deviation as in Rosenbaum and Rubin (1985). |

`mean.Tr` |
The mean of the treatment group. |

`mean.Co` |
The mean of the control group. |

`var.Tr` |
The variance of the treatment group. |

`var.Co` |
The variance of the control group. |

`p.value` |
The p-value from the two-sided weighted `t.test` . |

`var.ratio` |
var.Tr/var.Co. |

`ks` |
The object returned by `ks.boot` . |

`tt` |
The object returned by two-sided weighted
`t.test` . |

`qqsummary` |
The return object from a call to
`qqstats` with standardization—i.e., balance test
based on the empirical CDF. |

`qqsummary.raw` |
The return object from a call to
`qqstats` without standardization–i.e., balance tests
based on the empirical QQ-plot which retain the scale of the
variable. |

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

Rosenbaum, Paul R. and Donald B. Rubin. 1985. ``Constructing a Control
Group Using Multivariate Matched Sampling Methods That Incorporate the
Propensity Score.'' *The American Statistician* 39:1 33-38.

Hollander, Myles and Douglas A. Wolfe. 1973. *Nonparametric
statistical inference*. New York: John Wiley & Sons.

Also see `summary.balanceUV`

, `qqstats`

`ks.boot`

, `Match`

, `GenMatch`

,
`MatchBalance`

,
`GerberGreenImai`

, `lalonde`

data(lalonde) attach(lalonde) foo <- balanceUV(re75[treat==1],re75[treat!=1]) summary(foo)