Description Usage Arguments Details Value References See Also Examples

View source: R/212.CoverageProb_ADJ_All.R

Coverage Probability of Adjusted Wald-T method for given n

1 | ```
covpATW(n, alp, h, a, b, t1, t2)
``` |

`n` |
- Number of trials |

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

`h` |
- Adding factor |

`a` |
- Beta parameters for hypo "p" |

`b` |
- Beta parameters for hypo "p" |

`t1` |
- Lower tolerance limit to check the spread of coverage Probability |

`t2` |
- Upper tolerance limit to check the spread of coverage Probability |

Evaluation of approximate and adjusted method based on a t_approximation of the standardized point estimator using coverage probability, root mean square statistic, and the proportion of proportion lies within the desired level of coverage

A dataframe with

`mcpATW` |
Adjusted Wald-T Coverage Probability |

`micpATW ` |
Adjusted Wald-T minimum coverage probability |

`RMSE_N ` |
Root Mean Square Error from nominal size |

`RMSE_M ` |
Root Mean Square Error for Coverage Probability |

`RMSE_MI ` |
Root Mean Square Error for minimum coverage probability |

`tol ` |
Required tolerance for coverage probability |

[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.

Other Coverage probability of adjusted methods: `PlotcovpAAS`

,
`PlotcovpAAll`

, `PlotcovpALR`

,
`PlotcovpALT`

, `PlotcovpASC`

,
`PlotcovpATW`

, `PlotcovpAWD`

,
`covpAAS`

, `covpAAll`

,
`covpALR`

, `covpALT`

,
`covpASC`

, `covpAWD`

1 2 | ```
n= 10; alp=0.05; h=2;a=1;b=1; t1=0.93;t2=0.97
covpATW(n,alp,h,a,b,t1,t2)
``` |

proportion documentation built on May 29, 2017, 10:31 a.m.

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