Description Usage Arguments Value
Bayesian multiple Gaussian graphical models by MCMC.
1 |
dat |
a list of objets: n: number of observations. p: dimension of each pathway. K: number of pathways. z_P: indicator vector of genes membership P: dimension of the data. |
options |
a list of objets: burnin: number of MCMC iterations before burnin. nmc: number of MCMC iterations after burnin. |
PriorPar |
a list of objets: a: shape1 parameter for Theta for off-digonal block. b: shape2 parameter for Theta for off-digonal block. a0: shape1 parameter for Theta for digonal block. b0: shape2 parameter for Theta for digonal block. eps: rate parameter for v0^2. delta: shape parameter for v0^2. c: the parameter for decision boundary of spike-and-slab. Theta: a K x K initial graph PPI matrix. |
InitVal |
a list of objets: mu: intercept term. sigma2: overall noise level, same across groups. Beta: a P x P initial coefficient matrix. adj: a P x P initial adjacency matrix. |
a list of objets: Beta_save: p x p x K x nmc sample of coefficient matrix adj_save: p x p x K x nmc sample of adjacency matrix Theta_save: K x K x nmc sample of graph similarity matrix
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