Nested Effects Models to reconstruct phenotypic hierarchies


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Documentation for package `nem' version 1.6.0

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BFSlevel Build (generalized) hierarchy by Breath-First Search
BoutrosRNAi2002 RNAi data on Drosophila innate immune response
BoutrosRNAiDiscrete RNAi data on Drosophila innate immune response
BoutrosRNAiExpression RNAi data on Drosophila innate immune response
connectModules Infers a phenotypic hierarchy using the module network
enumerate.models Exhaustive enumeration of models
FULLmLL Full marginal likelihood of a phenotypic hierarchy
local.model.prior Computes a prior to be used for edge-wise model inference
mLL Marginal likelihood of a phenotypic hierarchy
moduleNetwork Infers a phenotypic hierarchy using the module network
nem Nested Effects Models - main function
nem.cont.preprocess Calculate classification probabilities of perturbation data according to control experiments
nem.discretize Discretize perturbation data according to control experiments
nemModelSelection model selection for nested effect models
network.AIC AIC criterion for network graph
pairwise.posterior Infers a phenotypic hierarchy edge by edge
plot.effects Plots data according to a phenotypic hierarchy
plot.ModuleNetwork plot nested effect model
plot.nem plot nested effect model
plot.pairwise plot nested effect model
plot.score plot nested effect model
plot.triples plot nested effect model
print.ModuleNetwork Infers a phenotypic hierarchy using the module network
print.pairwise Infers a phenotypic hierarchy edge by edge
print.score Computes the marginal likelihood of phenotypic hierarchies
print.triples Infers a phenotypic hierarchy from triples
SCCgraph Combines Strongly Connected Components into single nodes
score Computes the marginal likelihood of phenotypic hierarchies
subsets Subsets
transitive.closure Computes the transitive closure of a directed graph
transitive.reduction Computes the transitive reduction of a graph
triples.posterior Infers a phenotypic hierarchy from triples