zlambda: Compute normalisation factor for prior information.

In ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

Description

Compute normalisation factor for prior as defined in Wehrli/Husmeier 2007.

Usage

zlambda(B, lambda)

Arguments

B	The prior information matrix.
lambda	The strength of prior influence.

Details

Compute normalisation factor for Laplace prior as defined in Wehrli/Husmeier 2007.
Z = prod_v ( sum_pa(v) ( exp(-lambda * (sum_minpa(v)(1-B[v,m]) + sum_mnotinpa(v)(B[v,m])) ) ) ) = B[-v,pa(v)]

Value

Normalisation factor Z.

Note

Not used at the moment

Author(s)

Christian Bender

References

Wehrli and Husmeier 2007, Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge

See Also

TODO

Examples

## Not run: 
library(ddepn)
B <- matrix(runif(16),nrow=4,ncol=4,dimnames=list(LETTERS[1:4],LETTERS[1:4]))
zlambda(B,2.2)

## End(Not run)

ddepn documentation built on May 2, 2019, 4:42 p.m.