Re: [R] R function which finds confidence interval for binomial variance
Thanks Ralph, Moshe and [EMAIL PROTECTED] for you helpful comments. Using bootstrap (e.g., 'boot' + boot.ci()) for the confidence interval on the variance is not very accurate in coverage, because the sampling distribution is extremely skewed. In fact, the 'BCa' method returns the same result as the Efron 'percent' method. Moshe's idea of treating the confidence interval for the binomial variance as a transform of the confidence interval for the binomial proportion is elegant (Doh! Why didn't I think of that?), except that the transform is bivalued, although monotonic on each branch, with the branch point singularity at p=0.5. The bootstrap method does not have much coverage accuracy for any proportion, for n=6, 12 and 20, and the proportion method works great for n=6, 12, 20 and 50, except near p = 0.5, where it fails to achieve reasonable coverage. So I'm still looking for a reliable method for all p and for reasonable n. The proportion-based method is the best I've found, so far. Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED] Least Cost Formulations, Ltd.URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239Fax: 757-467-2947 Vere scire est per causas scire __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R function which finds confidence interval for binomial variance
Hello Robert, would it be an idea to construct CI's with bootstrap methods? If yes, you can use package boot, based on the book of Davison Hinkley or the package bootstrap, based on the book of Efron Tibshirani. You can put the estimator inside for argument theta. Bests, Ralph Am Thursday 25 September 2008 07:56:56 schrieb Robert A. LaBudde: I need to construct confidence intervals for the binomial variance. This is the usual estimate v = x*(n-x)/n or its unbiased counterpart v' = x*(n-x)/(n-1) where x = binomial number of successes observed in n Bernoulli trials from proportion p. The usual X^2 method for variance confidence intervals will not work, because of the strong non-normal character of the sampling distribution for v (or v'). Does anyone know of an R package with R function that computes a reasonable confidence interval for v or v'? Thanks. Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED] Least Cost Formulations, Ltd.URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239Fax: 757-467-2947 Vere scire est per causas scire __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] R function which finds confidence interval for binomial variance
I need to construct confidence intervals for the binomial variance. This is the usual estimate v = x*(n-x)/n or its unbiased counterpart v' = x*(n-x)/(n-1) where x = binomial number of successes observed in n Bernoulli trials from proportion p. The usual X^2 method for variance confidence intervals will not work, because of the strong non-normal character of the sampling distribution for v (or v'). Does anyone know of an R package with R function that computes a reasonable confidence interval for v or v'? Thanks. Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED] Least Cost Formulations, Ltd.URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239Fax: 757-467-2947 Vere scire est per causas scire __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
