Given a vector of observations with the number of samples falling in each class of a multinomial distribution,builds the simultaneous confidence intervals for the multinomial probabilities according to the method proposed by the mentioned authors.The R code for Sison and Glaz (1995) has been translated from thes SAS code written by May and Johnson (2000). By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. An experimenter may typically be interested in several quantiles of the distribution, such as the median, quartiles, and upper and lower tail quantiles, and this methodology provides a bridge between the confidence intervals with individual confidence levels and those that can be obtained from confidence bands. In addition, we show how the methods presented here for studying the differences among the multinomial parameters can be modified in order to obtain simultaneous confidence intervals for the relative differences among the multinomial parameters. We also present methods for obtaining simultaneous confidence intervals for the differences among the parameters of the multinomial distribution, and we compare these methods with the one suggested earlier by Gold (1963) for studying linear functions of the multinomial parameters. Description Usage Arguments Value Author(s) References See Also Examples. We also present methods for obtaining simultaneous confidence intervals for the differences among the parameters of the multinomial distribution, and we compare these methods with the one suggested earlier by Gold (1963) for studying linear functions of the multinomial parameters. download SAS/IML functions that are based on May and Johnson’s paper and macro. How to find confidence interval for Uniform([a,1])? Simultaneous Confidence Intervals and Sample Size Determination for Multinomial Proportions Cristina P. SISON and Joseph GLAZ* Simultaneous confidence interval procedures for multinomial proportions are used in many areas of science. In this article two new simultaneous confidence interval procedures are introduced. Some examples of the implementation of this nonparametric methodology are provided, and some comparisons are made with some parametric approaches to the problem. Its content features papers that describe new statistical techniques, illustrate innovative application of known statistical methods, or review methods, issues, or philosophy in a particular area of statistics or science, when such papers are consistent with the journal's mission. The neurotic proportion in the population is probably in the range [0.33, 0.50] and at the same time the depressed proportion is in the range [0.16, 0.30] and so forth. Comparison of 95% confidence intervals to the wider 99.35% confidence intervals used by Tukey's in the previous example. Why `bm` uparrow gives extra white space while `bm` downarrow does not? π1, π2, …, πk are the true population parameters. option. “A SAS macro for constructing simultaneous confidence intervals for multinomial proportions”. ©2000-2020 ITHAKA. To get the results in a matrix, call the MultCI function, as follows: The graph shows intervals that are likely to enclose all four parameters simultaneously. Confidence intervals with 99.35% individual confidence levels to obtain a 95% simultaneous confidence level using Tukey's. Log Transformations: How to Handle Negative Data Values? Abstract Simultaneous confidence interval procedures for multinomial proportions are used in many areas of science. Register to receive personalised research and resources by email, On Simultaneous Confidence Intervals for Multinomial Proportions, /doi/pdf/10.1080/00401706.1965.10490252?needAccess=true. The original May and Johnson macro contained a bug that was corrected in a later version (personal communication with Warren May, 25FEB2016). The original macro used SAS/IML version 6, so I have updated the program to use a more modern syntax. The purpose of this article is to show how simultaneous confidence intervals for several specified quantiles of the unknown distribution can be calculated using probabilities from a multinomial distribution. Here's one hack that *might* help: You can run -mlogit y- without any predictors. All Rights Reserved. The mission of Technometrics is to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences. Can the President of the United States pardon proactively? This article describes how to construct simultaneous confidence intervals for the proportions as described in the 1997 paper "A SAS macro for constructing simultaneous confidence intervals for multinomial proportions" by Warren May and William Johnson (Computer Methods and Programs in Biomedicine, p. 153–162). Making statements based on opinion; back them up with references or personal experience. My planet has a long period orbit. In the following, 1–α is the desired overall coverage probability for the confidence intervals, χ2(α, k-1) is the upper 1–α quantile of the χ2 distribution with k-1 degrees of freedom, and JSTOR®, the JSTOR logo, JPASS®, Artstor®, Reveal Digital™ and ITHAKA® are registered trademarks of ITHAKA. Because the cell counts are all relatively large and because the number of categories is relatively small, Goodman’s CIs should perform well. Compute simultaneous confidence intervals for the mean in SAS. How to get a smooth transition between startpoint and endpoint of a line in QGIS? If you have a random sample from a multinomial response, the sample proportions estimate the proportion of each category in the population. Maybe an MLE of a multinomial distribution? For the usual probability levels, we find that the confidence intervals introduced in the present article have the desirable property that they are shorter than the corresponding intervals obtained by the Gold method applied to the differences among the multinomial parameters. Glaz and Sison (Journal of Statistical Planning and Inference, 1999) contains formulae for the Sison and Glaz confidence intervals for the MLE, which simulation showed perform quite well, and also some parametric bootstrap confidence intervals, also for the MLEs. Numerical results are presented to evaluate these procedures and compare their performance with established methods that have been used in statistical literature. It only takes a minute to sign up. SAS-X.com offers news and tutorials about the various SAS® software packages, contributed by bloggers. I won't try to reproduce the math here, since there's rather a lot of it and it's in the paper anyway. The post Simultaneous confidence intervals for multinomial proportions appeared first on The DO Loop. We use cookies to improve your website experience. I wrote two “driver” functions: Let’s demonstrate how to call these functions on the psychiatric data. I can't figure out how to get -margins- to generate the prediction and CI for more than one outcome at a time, but you can do them serially: Mike, I think that what you are looking for is, http://cran.at.r-project.org/web/pac...tinomialCI.pdf, simultaneous confidence intervals for multinomial proportions, http://sites.google.com/a/lakeheadu.ca/bweaver/, https://www.stata.com/statalist/arch.../msg00483.html, You are not logged in.