Post_x {brr} R Documentation

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

### 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)
```

[Package brr version 1.0.0 Index]