bp: MCMC with bipartite graph
In wiscstatman/Rolemodel: Gene-set analysis via MFA

Description Usage Arguments Details Value Examples

MCMC with bipartite graph

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bp(whole, part, edge, alpha = 0.05, gamma = 0.2, p = 0.01,
  nburn = 10000, ngen = 100000, sub = 1000, penalty = 2,
  initial = c("inactive", "random", "high"))

`whole`	Vector of character strings with names of whole nodes (gene-sets)
`part`	Vector of 0's and 1's indicating active part nodes; names are the names of the part nodes (genes)
`edge`	Matrix with two columns; each row indicates an edge between a whole node and a part node
`alpha`	Parameter alpha (0 < alpha < gamma < 1)
`gamma`	Parameter gamma (0 < alpha < gamma < 1)
`p`	Parameter p (0 < p < 1)
`nburn`	Number of burn-in generations
`ngen`	Number of sample generations
`sub`	Subsample rate for burn-in and sample files
`penalty`	Penalty per illegal node to loglikelihood
`initial`	Initial state (see Details)

The initial argument can take one of three values: "inactive" - all whole nodes inactive; "random" - all whole nodes active with probability p, no illegal nodes; or "high" - all nodes with proportion of connected part nodes with response equal to 1 above 0.4 are active, no illegal nodes.

data frame with "whole" results;

`Name`	The names of whole nodes
`ActiveProbability`	The marginal posterior probabilities of whole nodes
`Count`	Number of active samples for each whole node
`Sample`	Number of generated samples for each whole node
`Degree`	The degree of each whole node
`Response`	The response of each whole node

dimburn + sample detail are included as an attribute, "samples", which is a matrix.

data(t2d)

## eout <- I2edge(t2d$I)

## bp.out <- bp(whole=colnames(t2d$I), part=t2d$y, edge=eout,
## alpha=0.00019, gamma=0.023, p=0.0033,
## nburn=1000000, ngen=10000000, sub=1000, penalty=5, initial="random")