# Nested Effects Models to reconstruct phenotypic hierarchies

## Documentation for package ‘nem’ version 2.4.0

## User Guides and Package Vignettes

Read overview or browse directory.

## Help Pages

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
bum.dalt | internal functions
bum.EM | internal functions
bum.histogram | internal functions
bum.mle | internal functions
bum.negLogLik | internal functions
bum.palt | internal functions
bum.qalt | internal functions
bum.ralt | internal functions
connectModules | internal functions
dbum | internal functions
enumerate.models | Exhaustive enumeration of models
filterEGenes | Automatic selection of most relevant S-genes
fitBUM | internal functions
FULLmLL | Full marginal likelihood of a phenotypic hierarchy
getComponent | internal functions
getDensityMatrix | Calculate density matrix from raw p-value matrix
getRelevantEGenes | Automatic selection of most relevant S-genes
internal | internal functions
inv.logit | internal functions
local.model.prior | Computes a prior to be used for edge-wise model inference
logit | internal functions
mLL | Marginal likelihood of a phenotypic hierarchy
moduleNetwork | Infers a phenotypic hierarchy using the module network
moduleNetwork.aux | internal functions
nem | Nested Effects Models - main function
nem.bootstrap | Bootstrapping for nested effect models
nem.calcSignificance | Statistical significance of network hypotheses
nem.cont.preprocess | Calculate classification probabilities of perturbation data according to control experiments
nem.discretize | Discretize perturbation data according to control experiments
nem.featureselection | internal functions
nem.greedy | Infers a phenotypic hierarchy using a greedy search strategy
nem.jackknife | Jackknife for nested effect models
nemModelSelection | model selection for nested effect models
network.AIC | AIC criterion for network graph
pairwise.posterior | Infers a phenotypic hierarchy edge by edge
pbum | internal functions
PhiDistr | Computes the marginal likelihood of phenotypic hierarchies
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.nem.greedy | Infers a phenotypic hierarchy using a greedy search strategy
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
prune.graph | Prunes spurious edges in a phenotypic hierarchy
qbum | internal functions
qqbum | internal functions
rbum | internal functions
SCCgraph | Combines Strongly Connected Components into single nodes
score | Computes the marginal likelihood of phenotypic hierarchies
selectEGenes | Automatic selection of most relevant S-genes
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