nem.greedy {nem} | R Documentation |
Starting from an initial graph (default: no edges), this strategy successively adds those edges, which most inrease the likelihood of the data under the model.
nem.greedy(D,initial=NULL,type="mLL",Pe=NULL,Pm=NULL,lambda=0,delta=1,para=NULL,hyperpara=NULL,verbose=TRUE) ## S3 method for class 'nem.greedy': print(x, ...)
D |
data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes. |
initial |
initial model to start greedy hillclimbing from (default: no edges) |
type |
see nem |
Pe |
prior position of effect reporters. Default: uniform over nodes in hierarchy |
Pm |
prior on model graph (n x n matrix) with entries 0 <= priorPhi[i,j] <= 1 describing the probability of an edge between gene i and gene j. |
lambda |
regularization parameter to incorporate prior assumptions. |
delta |
regularization parameter for automated E-gene subset selection (CONTmLLRatio only) |
para |
vector with parameters a and b for "mLL", if count matrices are used |
hyperpara |
vector with hyperparameters a0, b0, a1, b1 for "FULLmLL" |
verbose |
do you want to see progress statements printed or not? Default: TRUE |
x |
nem object |
... |
other arguments to pass |
nem object
Holger Froehlich
# Drosophila RNAi and Microarray Data from Boutros et al, 2002 data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] nem(D, para=c(.13,.05), inference="nem.greedy")