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=NULL,a=0.15,b=0.05,Pe=NULL,Pm=NULL,lambda=0,type="mLL")

### Arguments

`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) log-density matrix discribing the strength of an effect (iv) log-odds ratio matrix describing the strength of an effect
`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` | (i) "mLL" = marginal likelihood using count matrices (ii) "CONTmLL" = marginal likelihood for probability matrices (iii) "CONTmLLDens" = marginal likelihood for probability density matrices

### 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)

Holger Froehlich, 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 2.4.0 Index]