BMDU: Multinomial - Dirichlet (MD) model - UnEqual Prior - Bayes...

In CoinMinD: Simultaneous Confidence Interval for Multinomial Proportion

Description

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.

Usage

BMDU(x, d)

Arguments

x	x refers to the cell counts of given contingency table corresponding to a categorical data - non negative integers
d	d is the number of divisions required to split the prior vector of Dirichlet distribution to assign unequal values from U(0,1) and U(1,2)

Value

lower, upper limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals

Author(s)

Dr M Subbiah

See Also

BMDE,GM,WS

Examples

y=c(44,55,43,32,67,78)
z=2
BMDU(y,z)

Example output

Loading required package: MCMCpack
Loading required package: coda
Loading required package: MASS
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2019 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
Mean
[1] 0.1381 0.1699 0.1337 0.1043 0.2108 0.2433
Lower Limit
[1] 0.1031 0.1314 0.0992 0.0736 0.1685 0.1978
Upper Limit
[1] 0.1785 0.2122 0.1726 0.1397 0.2569 0.2930
Volume
[1] 2.487536e-07

CoinMinD documentation built on May 1, 2019, 10:32 p.m.