| rPosteriorPredictive.GaussianNIG | R Documentation |
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
## S3 method for class 'GaussianNIG' rPosteriorPredictive(obj, n, X, ...)
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. |
A matrix of n rows and nrow(X) columns, each row is a sample.
Banerjee, Sudipto. "Bayesian Linear Model: Gory Details." Downloaded from http://www. biostat. umn. edu/ ph7440 (2008).
GaussianNIG, dPosteriorPredictive.GaussianNIG
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)