empericalBA: The empirical Bayesian approach for Beta-Binomial model

In RajeswaranV/proportion: Inference on Single Binomial Proportion and Bayesian Computations

The empirical Bayesian approach for Beta-Binomial model

Description

The empirical Bayesian approach for Beta-Binomial model

Usage

empericalBA(n, alp, sL, sU)

Arguments

n	- Number of trials
alp	- Alpha value (significance level required)
sL	- Lower support for MLE optimization
sU	- Upper support for MLE optimization

Details

Highest Probability Density (HPD) and two tailed intervals are provided for all x = 0, 1, 2 ..n based on empirical Bayesian approach for Beta-Binomial model. Lower and Upper support values are needed to obtain the MLE of marginal likelihood for prior parameters.

Value

A dataframe with

x	- Number of successes (positive samples)
pomean	- Posterior mean
LBAQ	- Lower limits of Quantile based intervals
UBAQ	- Upper limits of Quantile based intervals
LBAH	- Lower limits of HPD intervals
UBAH	- Upper limits of HPD intervals

References

[1] 1998 Lehmann EL and Casella G Theory of Point Estimation, 2nd ed Springer, New York

See Also

Other Miscellaneous functions for Bayesian method: empericalBAx(), probPOSx(), probPOS(), probPREx(), probPRE()

Examples

sL=runif(1,0,2)				#Lower and upper of Support for MLE optimization
sU=runif(1,sL,10)
n= 5; alp=0.05
empericalBA(n,alp,sL,sU)

RajeswaranV/proportion documentation built on June 17, 2022, 9:11 a.m.