Quality of matches show how well matched pairs differ. For each variable the average distance is generated. Each item in a pair is assigned a group and after several iterations the quantile of these average distances is returned.

1 2 |

`covariate` |
A data.frame object. |

`matches` |
A data.frame or nonbimatch object. Contains information on how to match the covariate data set. |

`iterations` |
An integer. Number of iterations to run, defaults to 10,000. |

`probs` |
A numeric vector. Probabilities to pass to the quantile function. |

`use.se` |
A logical value. Determines if the standard error should be computed. Default value of FALSE. |

`all.vals` |
A logical value. Determines if false matches should be included in comparison. Default value of FALSE. |

`seed` |
Seed provided for random-number generation. Default value of 101. |

`...` |
Additional arguments, not used at the moment. |

This fuction is useful for determining the effectiveness of your weights
(when generating a distance matrix). Weighting a variable more will lower
the average distance, but it could penalize the distance of the other
variables. Calculating the standard error requires calling
`hdquantile`

from Hmisc. The quantiles may be slighly
different when using `hdquantile`

.

a list object containing elements with quality of match information

`q` |
data.frame with quantiles for each covariate |

`se` |
data.frame with standard error for each covariate |

`sd` |
vector with standard deviate for each covariate |

Cole Beck

1 2 3 4 5 6 | ```
df <- data.frame(id=LETTERS[1:25], val1=rnorm(25), val2=rnorm(25))
df.dist <- gendistance(df, idcol=1)
df.mdm <- distancematrix(df.dist)
df.match <- nonbimatch(df.mdm)
qom(df.dist$cov, df.match)
qom(df.dist$cov, df.match$matches)
``` |

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