mLL {nem}R Documentation

Marginal likelihood of a phenotypic hierarchy

Description

computes the marginal likelihood of observed phenotypic data given a phenotypic hierarchy.

Usage

mLL(Phi, D1, D0, a, b, Pe)

Arguments

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

Details

It computes the marginal likelihood of a single phenotypic hierarchy. Usually called from within the function score.

Value

mLL marginal likelihood of a phenotypic hierarchy
pos posterior distribution of effect positions in the hierarchy
mappos Maximum aposteriori estimate of effect positions

Author(s)

Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

References

Markowetz F, Bloch J, Spang R, Non-transcriptional pathway features reconstructed from secondary effects of RNA interference, Bioinformatics, 2005

See Also

score, FULLmLL

Examples

   data("BoutrosRNAi2002")
   result <- nem(BoutrosRNAiDiscrete[,9:16],type="mLL",para=c(.15,.05))

[Package nem version 1.2.0 Index]