mLL {nem} | R Documentation |
computes the marginal likelihood of observed phenotypic data given a phenotypic hierarchy.
mLL(Phi,D1,D0=NULL,a=0.15,b=0.05,Pe=NULL,Pm=NULL,lambda=0,type="mLL")
Phi |
an adjacency matrix with unit main diagonal |
D1 |
(i) count matrix for discrete data: phenotypes x genes. How often did we see an effect after interventions? (ii) matrix describing the PROBABILITIES of an effect (iii) matrix discribing the log-LIKELIHOOD of an effect (e.g. log-density matrix, log-odds matrix) |
D0 |
count matrix: phenotypes x genes. How often did we NOT see an effect after intervention? Not used for continious data |
a |
false positive rate: how probable is it to miss an effect? (for count matrix) |
b |
false negative rate: how probable is it to see a spurious effect? (for count matrix) |
Pe |
prior of effect reporter positions in the phenotypic 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. |
type |
see nem |
It computes the marginal likelihood of a single phenotypic hierarchy.
Usually called from within the function score
.
mLL |
marginal likelihood of a phenotypic hierarchy |
pos |
posterior distribution of effect positions in the hierarchy |
mappos |
Maximum aposteriori estimate of effect positions |
LLperGene |
likelihood per E-gene |
Holger Froehlich <URL: http://www.dkfz.de/mga2/people/froehlich>, Florian Markowetz <URL: http://genomics.princeton.edu/~florian>
Markowetz F, Bloch J, Spang R, Non-transcriptional pathway features reconstructed from secondary effects of RNA interference, Bioinformatics, 2005
data("BoutrosRNAi2002") result <- nem(BoutrosRNAiDiscrete[,9:16],type="mLL",para=c(.15,.05))