Description Usage Arguments Details Value References See Also Examples

View source: R/123.ConfidenceIntervals_CC_n_x.R

Continuity corrected Wald-T method of CI estimation

1 |

`x` |
- Number of successes |

`n` |
- Number of trials |

`alp` |
- Alpha value (significance level required) |

`c` |
- Continuity correction |

Approximate method based on a t_approximation of the standardized point estimator
using the test statistic
*(abs(phat-p)-c)/SE*
where *c > 0* is a constant for continuity correction for all *x = 0, 1, 2 ..n*.
Boundary modifications when *x = 0* or *x = n* using Wald adjustment method with
*h = 2*.

A dataframe with

`x` |
Number of successes (positive samples) |

`LCTWx ` |
T-Wald Lower limit |

`UCTWx ` |
T-Wald Upper Limit |

`LABB ` |
T-Wald Lower Abberation |

`UABB ` |
T-Wald Upper Abberation |

`ZWI ` |
Zero Width Interval |

[1] 1998 Agresti A and Coull BA. Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician: 52; 119 - 126.

[2] 1998 Newcombe RG. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine: 17; 857 - 872.

[3] 2008 Pires, A.M., Amado, C. Interval Estimators for a Binomial Proportion: Comparison of Twenty Methods. REVSTAT - Statistical Journal, 6, 165-197.

`prop.test and binom.test`

for equivalent base Stats R functionality,
`binom.confint`

provides similar functionality for 11 methods,
`wald2ci`

which provides multiple functions for CI calculation ,
`binom.blaker.limits`

which calculates Blaker CI which is not covered here and
`propCI`

which provides similar functionality.

Other Continuity correction methods of CI estimation given x and n: `PlotciCAllxg`

,
`PlotciCAllx`

, `ciCAllx`

,
`ciCLTx`

, `ciCSCx`

,
`ciCWDx`

1 2 |

```
x LCTWx UCTWx LABB UABB ZWI
1 5 0 1 YES YES NO
```

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