| BMNormalPop {Baldur} | R Documentation |
BMNormalPop produces iid draws from the posterior density of a Bayesian Multivariate Normal Population model with a Multivariate Normal prior for the mean parameters and a
Wishart Prior for the data variance-covariance matrix.
BMNormalPop(y, mu, Var,Var0,n0,draws, Prec = FALSE,A=FALSE)
y |
n*m Matrix with observed data points |
mu |
m*1 Prior mean Matrix |
Var |
m*m Prior Variance-Covariance Matrix |
Var0 |
m*m Matrix with Prior Point Estimate for data Variance-Covariance Matrix |
n0 |
Number of prior observations for data Variance-Covariance Matrix |
draws |
Number of desired draws from the posterior density |
Prec |
Optional logical argument with default Prec=FALSE. If argument is set to TRUE, Var is treated as the prior precision. |
A |
Optional logical argument with default A=FALSE. If argument is set to TRUE, the function returns not only the simulated draws from the posterior density but also a vector with information related to the acceptance rate for the underlying accept/reject procedure (see below). |
Makes use of the likelihood subgradient density accept/reject procedure of Nygren and Nygren (2006) in order to generate iid samples from the posterior density of a Poisson regression model with a multivariate normal prior. If the posterior density is close to multivariate normal, then the expected number of draws should be approximately equal to $(2/sqrt{pi})^{k}$.
If A=FALSE, a matrix beta. If A=TRUE, a list containing the matrix beta and a matrix Accept.:
beta |
draws*k matrix with the iid draws for the model parameters. |
Accept |
draws*1 matrix with the number of candidates for each draw that were required before acceptance in the accept/reject procedure. |
Kjell Nygren knygren@us.imshealth.com