FULLmLL {nem} | R Documentation

## Full marginal likelihood of a phenotypic hierarchy

### Description

The function the full marginal likelihood of a phenotypic hierarchy. The full marginal likelihood equals the marginal likelihood `mLL` averaged over the error probabilities $α$ and $β$.

### Usage

    FULLmLL(Phi, D1, D0, a0, b0, a1, b1, Pe, Pm=NULL, lambda=0)

### 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?
`a0, b0, a1, b1` | Hyperparameters
`Pe` | prior of effect positions in the hierarchy. A matrix of size phenotypes x genes, where each row contains positive numbers summing to 1.
`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.

### Details

Additionally to the marginal likelihood introduced in Markowetz et al (2005), we can average over the error probabilities $α$ and $β$ assuming Beta priors. The parameters of the two Beta priors are hyperparameters of the full marginal likelihood score. The four hyperparameters fall into two categories: `a1` and `b0` are weights for observing the predicted state, while `a0` and `b1` are weights for observing errors. We suggest setting `a1=b0` and `a0=b1`. The ratio between the two values should correspond to our assessment of the noise level. See the example section for an application. The function `FULLmLL` is usually called from within function `score`.

### Value

`mLL` | full marginal likelihood of a model
`pos` | posterior distribution of effect positions in the hierarchy
`mappos` | maximum aposteriori estimate of effect positions
`LLperGene` | likelihood per E-gene

### Author(s)

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

### References

Markowetz F, Probabilistic Models for Gene Silencing Data. PhD thesis, Free University Berlin, 2006.

### See Also

`nem`, `score`, `mLL`

### Examples

       data("BoutrosRNAi2002")
       res <- nem(BoutrosRNAiDiscrete[,9:16],type="FULLmLL",hyperpara=c(1,9,9,1))

[Package nem version 2.6.0 Index]