Description Usage Arguments Value References See Also Examples

Dependency detection between level *k* (*k > 1*) categorical variable and continuous variable via dynamic slicing with *O(n^{1/2})*-resolution. The basic idea is almost the same as `ds_k`

. The only different is that `ds_eqp_k`

groups samples into approximate *O(n^{1/2})* groups which contain approximate *O(n^{1/2})* samples and performs dynamic slicing on their boundaries. This much faster version could reduce computation time substantially without too much power loss. Based on the strategy of `ds_eqp_k`

, we recommend to apply it in large sample size problem and use `ds_k`

for ordinary problem. For more details please refer to Jiang, Ye & Liu (2015). Results contains value of dynamic slicing statistic and slicing strategy. It could be applied for non-parametric *K*-sample hypothesis testing.

1 |

`x` |
Vector: observations of categorical variable, |

`xdim` |
Level of |

`lambda` |
Penalty for introducing an additional slice, which is used to avoid making too many slices. It corresponds to the type I error under the scenario that the two variables are independent. |

`slice` |
Indicator for reporting slicing strategy or not. |

`dsval` |
Value of dynamic slicing statistic. It is nonnegative. If it equals zero, the categorical variable and continuous variable will be treated as independent of each other, otherwise they will be treated as dependent. |

`slices` |
Slicing strategy that maximize dynamic slicing statistic based on currently ranked vector |

Jiang, B., Ye, C. and Liu, J.S. Non-parametric *K*-sample tests via dynamic slicing. *Journal of the American Statistical Association*, 110(510): 642-653, 2015.

`ds_k`

.

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