getfirstphi: Helper function for netga.

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

Description

Internal helper. Sample an initial network.

Usage

getfirstphi(x, dat, stimuli, V, tps, reps, hmmiterations,
 lambda=NULL, B=NULL, Z=NULL, fanin=4, gam=NULL, it=NULL, 
 K=NULL, priortype="none", scale_lik=FALSE,
 allow.stim.off=TRUE, implementation="C")

Arguments

x	A candidate network, given as adjacency matrix.
dat	The data matrix.
stimuli	The input stimuli list.
V	The names of the nodes.
tps	The time points.
reps	The number of replicates.
hmmiterations	The maximum number of iterations for the HMM.
lambda	The Prior influence strength in the laplace prior.
B	The Prior information matrix. Corresponds to prior edge probabilities in the final network.
Z	The normalisation factor for the prior distribution.
fanin	Integer: maximal indegree for nodes.
gam	Prior influence strength in scalefree prior.
it	Number of iterations to generate background distribution in scalefree prior.
k	Proportionality factor in scalefree prior
priortype	Character. Type of prior to be used.
scale_lik	Boolean. Scale liklihood according to number of data points.
allow.stim.off	Boolean. Allow the stimuli to become inactive at some point. See also ddepn.
implementation	String. One of "C","R","R_globalest","C_globalest". Different implementations of the HMM in perform.hmmsearch. If "R", the original pure R-implementation is used, if "C", a ported C-implementation is used. If "R_globalest", an experimental version of the parameter estimation is used in the HMM, "C_globalest" is the C-port of this version. See details for a description.

Details

Usually called internally.

Value

TODO

Note

TODO

Author(s)

Christian Bender

References

TODO

See Also

ddepn

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