Description Usage Arguments Details Value References See Also Examples

Vectorized implementation of confidence intervals

1 2 |

`x1` |
Mismatch counts in the test sample. |

`n1` |
Sequencing depth (total counts) in the test sample. |

`x2` |
Mismatch counts in the control sample. |

`n2` |
Sequencing depth (total counts) in the control sample. |

`conf_level` |
Confidence level $beta$ (default: 0.95). |

`clip` |
Should the CIs be clipped to the interval [-1,1] if they exceed this? |

`split` |
Should the sample split method be applied? See 'splitSampleBinom' for details. |

These functions implement a vectorized version of the two-sided Agresti-Caffo, and Newcombe-Hybrid-Score confidence interval for the difference of two binomial proportions.

A data frame with columns

dEstimate for the difference of rates 'p1' and 'p2'.

p1, p2Estimates for the mismatches rates for each sample.

lower, upperLower and upper bound of the confidence interval.

wWidth of the confidence interval.

Agresti, Alan, and Brian Caffo. Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures. The American Statistician 54, no. 4 (2000): 280–288

Newcombe, Robert G. Interval Estimation for the Difference between Independent Proportions: Comparison of Eleven Methods. Statistics in Medicine 17, no. 8 (1998): 873–890.

Fagerland, Morten W., Stian Lydersen, and Petter Laake. Recommended Confidence Intervals for Two Independent Binomial Proportions. Statistical Methods in Medical Research (2011).

nhsCi

splitSampleBinom

binMto::Add4 binMto::NHS

1 2 3 4 5 6 7 8 9 10 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.