ModeDeltaPoisNPP {NPP} | R Documentation |
Calculate Posterior Mode of the Power Parameter in Normalized Power Prior with Grid Search, Poisson Population
Description
The function returns the posterior mode of the power parameter \delta
in multinomial population.
It calculates the log of the posterior density (up to a normalizing constant), and conduct a grid search
to find the approximate mode.
Usage
ModeDeltaPoisNPP(Data.Cur, Data.Hist,
CompStat = list(n0 = NULL, mean0 = NULL, n1 = NULL, mean1 = NULL),
npoints = 1000, prior = list(lambda.shape = 1/2,
lambda.scale = 100, delta.alpha = 1, delta.beta = 1))
Arguments
Data.Cur |
a non-negative integer vector of each observed current data. |
Data.Hist |
a non-negative integer vector of each observed historical data. |
CompStat |
a list of four elements that represents the
"compatibility(sufficient) statistics" for
|
npoints |
is a non-negative integer scalar indicating number of points on a regular spaced grid between [0, 1], where we calculate the log of the posterior and search for the mode. |
prior |
a list of the hyperparameters in the prior for both
|
Details
See example.
Value
A numeric value between 0 and 1.
Author(s)
Zifei Han hanzifei1@gmail.com
References
Ibrahim, J.G., Chen, M.-H., Gwon, Y. and Chen, F. (2015). The Power Prior: Theory and Applications. Statistics in Medicine 34:3724-3749.
Duan, Y., Ye, K. and Smith, E.P. (2006). Evaluating Water Quality: Using Power Priors to Incorporate Historical Information. Environmetrics 17:95-106.
See Also
ModeDeltaBerNPP
;
ModeDeltaNormalNPP
;
ModeDeltaMultinomialNPP
Examples
ModeDeltaPoisNPP(CompStat = list(n0 = 50, mean0 = 10, n1 = 50, mean1 = 10), npoints = 1000,
prior = list(lambda.shape = 1/2, lambda.scale = 100,
delta.alpha = 1, delta.beta = 1))
ModeDeltaPoisNPP(CompStat = list(n0 = 50, mean0 = 10, n1 = 50, mean1 = 9.5), npoints = 1000,
prior = list(lambda.shape = 1/2, lambda.scale = 100,
delta.alpha = 1, delta.beta = 1))
ModeDeltaPoisNPP(CompStat = list(n0 = 50, mean0 = 10, n1 = 50, mean1 = 9), npoints = 1000,
prior = list(lambda.shape = 1/2, lambda.scale = 100,
delta.alpha = 1, delta.beta = 1))