nem.greedyMAP {nem} | R Documentation |
Starting with an initial estimate of the linking of E-genes to S-genes from the data, this method performs an alternating MAP optimization of the S-genes graph and the linking graph until convergence. As a final step the function closest.transitive.greedy
can be invoked to find a transitively closed graph most similar to the original one.
nem.greedyMAP(D,Pe=NULL,Pm=NULL,lambda=0,delta=1, trans.close=TRUE, verbose=TRUE) ## S3 method for class 'nem.greedyMAP': print(x, ...)
D |
data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes. |
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 |
trans.close |
find a similar transitively closed graph |
verbose |
do you want to see progress statements printed or not? Default: TRUE |
x |
nem object |
... |
other arguments to pass |
nem object
Holger Froehlich
nem
, closest.transitive.greedy
data("BoutrosRNAi2002") res <- nem(BoutrosRNAiLods, inference="nem.greedyMAP", delta=0) # plot graph plot(res,what="graph") # plot posterior over effect positions plot(res,what="pos") # estimate of effect positions res$mappos