Post_y {brr} R Documentation

## Posterior predictive distribution of the count in the control group

### Description

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

### Usage

```dpost_y(ynew, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)

ppost_y(q, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)

qpost_y(p, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)

rpost_y(n, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)

spost_y(Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T, ...)
```

### Arguments

 `ynew,q` vector of non-negative integer quantiles `a,b` non-negative shape parameter and rate 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 `T,Tnew` sample sizes of the control 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_y` gives the density, `ppost_y` the distribution function, `qpost_y` the quantile function, `rpost_y` samples from the distribution, and `spost_y` gives a summary of the distribution.

### Note

`Post_y` is a generic name for the functions documented.

### Examples

```barplot(dpost_y(0:10, 10, 2, 7, 3, 4, 5, 3, 10))
spost_y(10, 2, 7, 3, 4, 5, 3, 10, output="pandoc")
```

[Package brr version 1.0.0 Index]