probPREx: The Predicted probability - Bayesian approach

In proportion: Inference on Single Binomial Proportion and Bayesian Computations

Description

The Predicted probability - Bayesian approach

Usage

probPREx(x, n, xnew, m, a1, a2)

Arguments

x	- Number of successes
n	- Number of trials from data
xnew	- Required size of number of success
m	- Future :Number of trials
a1	- Beta Prior Parameters for Bayesian estimation
a2	- Beta Prior Parameters for Bayesian estimation

Details

Computes posterior predictive probability for the required size of number of successes for xnew of m trials from the given number of successes x of n trials for the given parameters for Beta prior distribution

Value

A dataframe with x,n,xnew,m,preprb

x	Number of successes
n	Number of trials
xnew	Required size of number of success
m	Future - success, trails
preprb	The predicted probability

References

[1] 2002 Gelman A, Carlin JB, Stern HS and Dunson DB Bayesian Data Analysis, Chapman & Hall/CRC

See Also

Other Miscellaneous functions for Bayesian method: empericalBAx, empericalBA, probPOSx, probPOS, probPRE

Examples

x=0; n=1; xnew=10; m=10; a1=1; a2=1
probPREx(x,n,xnew,m,a1,a2)

proportion documentation built on May 1, 2019, 7:54 p.m.