A-package-for-binomial-proportion: Proportion: Let x denote the number of successes in n...

Description Introduction Proportion methods grouping Proportion function naming convention Reproducibility of reference papers References


Proportion: Let x denote the number of successes in n independent Bernoulli trials with X ~ Binomial (n, p) then phat = x/n denotes the sample proportion.


Objective of this package is to present interval estimation procedures for 'p' outlined above in a more comprehensive way.Quality assessment procedures such as statistic based on coverage probability, Expected length, Error, p-confidence and p-bias are also included. Also, an array of Bayesian computations (Bayes factor, Empirical Bayesian, Posterior predictive computation, and posterior probability) with conjugate prior are made available. The proportion package provides three categories of important functions: Confidence Intervals, metrics on confidence intrvals (coverage probability, length, p-confidence and p-bias, error and long term power) and other methods (hypothesis testing and general/simulation methods).

Proportion methods grouping

For finding confidence interval for p we have included

Proportion function naming convention

The general guideline for finding functions are given below:

Reproducibility of reference papers

To help the researcher reporduce results in existing papers we have taken six key papers (see references below) [3], [8], [9], [10], [11], [12] and reproduced the results and suggested further items to try. Details are in the vignette.


[1] 1993 Vollset SE. Confidence intervals for a binomial proportion. Statistics in Medicine: 12; 809 - 824.

[2] 1998 Agresti A and Coull BA. Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician: 52; 119 - 126.

[3] 1998 Newcombe RG. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine: 17; 857 - 872.

[4] 2001 Brown LD, Cai TT and DasGupta A. Interval estimation for a binomial proportion. Statistical Science: 16; 101 - 133.

[5] 2002 Pan W. Approximate confidence intervals for one proportion and difference of two proportions Computational Statistics and Data Analysis 40, 128, 143-157.

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

[7] 2014 Martin Andres, A. and Alvarez Hernandez. M. Two-tailed asymptotic inferences for a proportion. Journal of Applied Statistics, 41, 7, 1516-1529

[8] 2005 Vos PW and Hudson S. Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length. The American Statistician: 59; 137 - 142.

[9] 2005 Joseph L and Reinhold C. Statistical Inference for Proportions American Journal of Radiologists 184; 1057 - 1064

[10] 2008 Zhou, X. H., Li, C.M. and Yang, Z. Improving interval estimation of binomial proportions. Phil. Trans. R. Soc. A, 366, 2405-2418

[11] 2012 Wei Yu, Xu Guo and Wangli Xua. An improved score interval with a modified midpoint for a binomial proportion, Journal of Statistical Computation and Simulation, 84, 5, 1-17

[12] 2008 Tuyl F, Gerlach R and Mengersen K . A comparison of Bayes-Laplace, Jeffreys, and Other Priors: The case of zero events. The American Statistician: 62; 40 - 44.

proportion documentation built on May 1, 2019, 7:54 p.m.