This method provides 95 percent simultaneous confidence interval for multinomial proportions based on Bayesian Multinomial Dirichlet model. However, it provides a mechanism through which user can split the Dirichlet prior parameter vector and suitable distributions can be incorporated for each of two groups.
scimp_bmdu(x, d, seed = 1492)
x | cell counts of given contingency table corresponding to a categorical data - non negative integers |
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d | number of divisions required to split the prior vector of Dirichlet distribution to assign unequal values from U(0,1) and U(1,2) |
seed | random seed for reproducible results |
tibble
with original limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals and the mean
Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2002). Bayesian Data Analysis. Chapman & Hall, London.
y <- c(44, 55, 43, 32, 67, 78) z <- 2 scimp_bmdu(y, z)