Post_x: Posterior predictive distribution of the count in the treated...

In brr: Bayesian Inference on the Ratio of Two Poisson Rates

Description

Density, distribution function, quantile function and random generation for the posterior predictive distribution of the count in the treated group.

Usage

dpost_x(xnew, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)

ppost_x(q, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)

qpost_x(p, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)

rpost_x(n, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)

spost_x(Snew, a = 0.5, c = 0.5, d = 0, x, y, S, ...)

Arguments

xnew,q	vector of non-negative integer quantiles
a	non-negative shape parameter of the Gamma prior distribution on the rate μ
c,d	non-negative shape parameters of the prior distribution on φ
x,y	counts (integer) in the treated group and control group of the observed experiment
S,Snew	sample sizes of the treated group in the observed experiment and the predicted experiment
p	vector of probabilities
n	number of observations to be simulated
...	arguments passed to summary_PGIB

Details

The posterior predictive distribution of the count in the treated group is a Poisson-Gamma-Inverse Beta distribution.

Value

dpost_x gives the density, ppost_x the distribution function, qpost_x the quantile function, rpost_x samples from the distribution, and spost_x gives a summary of the distribution.

Note

Post_x is a generic name for the functions documented.

Examples

barplot(dpost_x(0:10, 10, 2, 3, 4, 5, 3, 10))
qpost_x(0.5, 10, 2, 3, 4, 5, 3, 10)
ppost_x(4, 10, 2, 3, 4, 5, 3, 10)

brr documentation built on May 2, 2019, 1:04 a.m.