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

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

score, mLL

Examples

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

[Package nem version 2.2.1 Index]