local.model.prior {nem} | R Documentation |
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.
local.model.prior(size,n,bias)
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 |
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.
a distribution over four states: a vector of four positive real numbers summing to one
Florian Markowetz <URL: http://genomics.princeton.edu/~florian>
# uniform over the 3 edge models local.model.prior(4,4,1) # bias towards <-> local.model.prior(4,4,2)