Description Usage Arguments Value See Also Examples

This function tests whether covariates in a treatment group and a matched control group are balanced in observational studies through the nearest neighbor graph constructed on the subjects.

1 |

`distM` |
The distance matrix for the pooled observations (pooled over the treated subjects and the matched controls). If there are n treated subjects and n matched controls, then this distance matrix is a 2n by 2n matrix with the [i,j] element the distance between observation i and observation j. What distance to use is decided by users. Some simple choices are the Euclidean distance, L1 distance, and mahalanobis distance. |

`treated.index` |
The subject indices of the treated subjects. The subjects are ordered in the same way as for calculating the distance matrix, distM. |

`perm` |
The number of permutations performed to calculate the p-value of the test. The default value is 0, which means the permutation is not performed and only approximate p-value based on asymptotic theory is provided. Doing permutation could be time consuming, so be cautious if you want to set this value to be larger than 10,000. |

`k` |
Set as positive integer values, indicates k-NN is used. |

`discrete.correction` |
When this is set as TRUE (recommended), a continuation correction is done for computing the asymptotic p-value to account for the discrete nature of the statistic. |

`test.stat.Z` |
The standardized test statistic (ZD in the reference paper. |

`pval.appr` |
The approximated p-value based on asymptotic theory. |

`pval.perm` |
The permutation p-value when argument 'perm' is positive. |

1 2 3 4 5 6 7 8 | ```
## A snippet of the smoking example in the reference paper.
## smoking.rda contains a 300 by 300 distance matrix, smokingDist.
## The indices of the treated subjects are 1:150.
data(smoking)
CrossNN(smokingDist, 1:150)
## Uncomment the following line to get permutation p-value with 1,000 permutations.
# CrossNN(smokingDist, 1:150, perm=1000)
``` |

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