nemModelSelection {nem} | R Documentation |
Infers models with different regularization constants, compares them via the AIC criterion and returns the highest scoring one
nemModelSelection(lambdas,D,inference="nem.greedy",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pmlocal=NULL,Pm=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,triples.thrsh=0.5,delta=1,selEGenes=FALSE,trans.close=TRUE,verbose=TRUE,...)
lambdas |
vector of regularization constants |
D |
data matrix with experiments in the columns (binary or continious) |
inference |
search to use exhaustive enumeration; or triples for triple-based inference; or pairwise for the pairwise heuristic; or ModuleNetwork for the module based inference; or nem.greedy for the greedy hillclimbing |
models |
a list of adjacency matrices for model search. If NULL, enumerate.models is used for exhaustive enumeration of all possible models. |
type |
mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP , see nem |
para |
vector of length two: false positive rate and false negative rate for binary data. Used by mLL |
hyperpara |
vector of length four: used by FULLmLL for binary data |
Pe |
prior of effect reporter positions in the phenotypic hierarchy (same dimension as D) |
Pm |
prior over models (n x n matrix) |
Pmlocal |
local model prior for pairwise and triple learning. For pairwise learning generated by local.model.prior according to arguments local.prior.size and local.prior.bias |
local.prior.size |
prior expected number of edges in the graph (for pairwise learning) |
local.prior.bias |
bias towards double-headed edges. Default: 1 (no bias; for pairwise learning) |
triples.thrsh |
threshold for model averaging to combine triple models for each edge |
delta |
regularization parameter for automated E-gene subset selection (CONTmLLMAP only) |
selEGenes |
automated E-gene subset selection (includes tuning of delta for CONTmLLMAP) |
trans.close |
Should always transitive closed graphs be computed? Default: TRUE. NOTE: This has only an impact for the nem.greedyMAP method. |
verbose |
do you want to see progression statements? Default: TRUE |
... |
other arguments to pass to function nem or network.AIC |
nemModelSelection
internally calls nem
to infer a model with a given regularization constant. The comparison between models is based on the BIC or AIC criterion, depending on the parameters passed to network.AIC
.
nem object
Holger Froehlich
data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] p <- c(.13,.05) res <- nemModelSelection(c(0.1,1,10),D, para=p, Pm=matrix(0,ncol=4,nrow=4)) plot(res,main="highest scoring model")