local.model.prior {nem} | R Documentation

## Computes a prior to be used for edge-wise model inference

### Description

The function `pairwise.posterior` infers a phenotypic hierarchy edge by edge by choosing between four models (unconnected, subset, superset, undistinguishable). For each edge, `local.model.prior` computes a prior distribution over the four models. It can be used to ensure sparsity of the graph and high confidence in results.

### Usage

    local.model.prior(size,n,bias)

### Arguments

`size` | expected number of edges in the graph.
`n` | number of perturbed genes in the dataset, number of nodes in the graph
`bias` | the factor by which the double-headed edge is preferred over the single-headed edges

### Details

A graph on `n` nodes has `N=n*(n-1)/2` possible directed edges (one- or bi-directional). If each edge occurs with probability $p$, we expect to see $Np$ edges in the graph. The function `local.model.prior` takes the number of genes (`n`) and the expected number of edges (`size`) as an input and computes a prior distribution for edge occurrence: no edge with probability `size/N`, and the probability for edge existence being split over the three edge models with a bias towards the conservative double-headed model specified by `bias`. To ensure sparsity, the `size` should be chosen small compared to the number of possible edges.

### Value

a distribution over four states: a vector of four positive real numbers summing to one

### Note

### Author(s)

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

### References

### See Also

`pairwise.posterior`, `nem`

### Examples

    # uniform over the 3 edge models
    local.model.prior(4,4,1)
    # bias towards <->
    local.model.prior(4,4,2)

[Package nem version 1.6.0 Index]