Description Usage Arguments Details Value References See Also Examples

View source: R/111.ConfidenceIntervals_ADJ_n.R

Adjusted Likelihood method of CI estimation

1 | ```
ciALR(n, alp, h)
``` |

`n` |
- Number of trials |

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

`h` |
- adding factor |

Likelihood ratio limits for the data *x + h* and *n + (2*h)*
instead of the given codex and `n`

, where `h`

is a positive integer
*(1, 2.)* and for all *x = 0, 1, 2 ..n.*

A dataframe with

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

`LALR ` |
Adjusted Likelihood Lower limit |

`UALR ` |
Adjusted Likelihood Upper Limit |

`LABB ` |
Adjusted Likelihood Lower Abberation |

`UABB ` |
Adjusted Likelihood 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 Adjusted methods of CI estimation: `PlotciAAS`

,
`PlotciAAllg`

, `PlotciAAll`

,
`PlotciALR`

, `PlotciALT`

,
`PlotciASC`

, `PlotciATW`

,
`PlotciAWD`

, `ciAAS`

,
`ciAAll`

, `ciALT`

,
`ciASC`

, `ciATW`

,
`ciAWD`

1 2 | ```
n=5; alp=0.05;h=2
ciALR(n,alp,h)
``` |

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