bbricks (version 0.1.4)

rPosteriorPredictive.GaussianNIG: Generate random samples from the posterior predictive distribution of a "GaussianNIG" object

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

# S3 method for 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).

See Also

GaussianNIG, dPosteriorPredictive.GaussianNIG

Examples

# NOT RUN {
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)
# }