mLL {nem} | R Documentation |
computes the marginal likelihood of observed phenotypic data given a phenotypic hierarchy.
mLL(Phi, D1, D0, a, b, Pe)
Phi |
an adjacency matrix with unit main diagonal |
D1 |
count matrix: phenotypes x genes. How often did we see an effect after interventions? |
D0 |
count matrix: phenotypes x genes. How often did we NOT see an effect after intervention? |
a |
false positive rate: how probable is it to miss an effect? |
b |
false negative rate: how probable is it to see a spurious effect? |
Pe |
prior of effect reporter positions in the phenotypic hierarchy |
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 |
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))