Description Usage Arguments Details Value References See Also Examples

View source: R/113.ConfidenceIntervals_ADJ_n_x.R

AdjustedWALD-T method of CI estimation

1 | ```
ciATWx(x, n, alp, h)
``` |

`x` |
- Number of successes |

`n` |
- Number of trials |

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

`h` |
- Adding factor |

Given data `x`

and `n`

are modified as *x + h* and *n + (2*h)*
respectively, where *h > 0* then approximate method based on a t_approximation of the
standardized point estimator for the given `x`

and `n`

.

A dataframe with

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

`LATWx ` |
T-Wald Lower limit |

`UATWx ` |
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 Adjusted methods of CI estimation given x & n: `PlotciAAllx`

,
`ciAASx`

, `ciAAllx`

,
`ciALRx`

, `ciALTx`

,
`ciASCx`

, `ciAWDx`

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

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