# rPosteriorPredictive.GaussianNIG: Generate random samples from the posterior predictive... In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

Generate random samples from the posterior predictive distribution of the following structure:

x \sim Gaussian(X beta,sigma^2)

sigma^2 \sim InvGamma(a,b)

beta \sim Gaussian(m,sigma^2 V)

Where X is a row vector, or a design matrix where each row is an obervation. InvGamma() is the Inverse-Gamma distribution, Gaussian() is the Gaussian distribution. See `?dInvGamma` and `dGaussian` for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIG" object.
Posterior predictive is a distribution of x|m,V,a,b,X

## Usage

 ```1 2``` ```## S3 method for class 'GaussianNIG' rPosteriorPredictive(obj, n, X, ...) ```

## Arguments

 `obj` A "GaussianNIG" object. `n` integer, number of samples. `X` matrix, the location of the prediction, each row is a location. `...` Additional arguments to be passed to other inherited types.

## Value

A matrix of n rows and nrow(X) columns, each row is a sample.

## References

Banerjee, Sudipto. "Bayesian Linear Model: Gory Details." Downloaded from http://www. biostat. umn. edu/ ph7440 (2008).

`GaussianNIG`, `dPosteriorPredictive.GaussianNIG`
 ```1 2 3``` ```obj <- GaussianNIG(gamma=list(m=c(1,1),V=diag(2),a=1,b=1)) X <- matrix(runif(20),ncol=2) rPosteriorPredictive(obj=obj,n=3,X=X) ```