set.default.parameters {nem}R Documentation

Get/set hyperparameters

Description

Allows to set and retrieve various hyperparameters for different inference methods.

Usage

set.default.parameters(Sgenes, ...)

Arguments

Sgenes character vector of S-gene identifiers
... parameters to set (see details)

Details

Since version 2.5.4 functions in the nem package do not have any more a large amount of individual parameters. Instead there is just one hyperparameter, which is passed to all functions. Parameter values with the hyperparameter can be set with this function.

type
mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP or gnem. CONTmLLDens and CONTmLLRatio are identical to CONTmLLBayes and CONTmLLMAP and are still supported for compatibility reasons. mLL and FULLmLL are used for binary data (see BoutrosRNAiDiscrete) and CONTmLL for a matrix of effect probabilities. CONTmLLBayes and CONTmLLMAP are used, if log-odds ratios, p-value densities or any other model specifies effect likelihoods. CONTmLLBayes refers to an inference scheme, were the linking positions of effect reporters to network nodes are integrated out, and CONTmLLMAP to an inference scheme, were a MAP estimate for the linking positions is calculated. gnem indicates Generalized Nested Effects Models (GNEMs).
para
vector of length two: false positive rate and false negative rate for binary data. Used by mLL
hyperpara
vector of length four: used by FULLmLL() for binary data
Pe
prior of effect reporter positions in the phenotypic hierarchy (same dimension as D). Not used type gnem. Default: NULL
Pm
prior over models (n x n matrix). Default: NULL
Pmlocal
local model prior for pairwise and triple learning. For pairwise learning generated by local.model.prior according to arguments local.prior.size and local.prior.bias
local.prior.size
prior expected number of edges in the graph (for pairwise learning). Default: no. nodes
local.prior.bias
bias towards double-headed edges. Default: 1 (no bias; for pairwise learning)
triples.thrsh
threshold for model averaging to combine triple models for each edge. Default: 0.5
lambda
regularization parameter to incorporate prior assumptions. May also be a vector of possible values, if nemModelSelection is used, Default: 0 (no regularization)
delta
regularization parameter for automated subset selection of effect reporters (CONTmLLMAP only). Default: 1/no. nodes
selEGenes
automated E-gene subset selection (includes tuning of delta for CONTmLLMAP). Default: FALSE
trans.close
Should always transitive closed graphs be computed? Default: TRUE. NOTE: This has only an impact for type nem.greedyMAP and gnem. Default: TRUE
backward.elimination
For module networks and greedy hillclimbing inference: Try to eliminate edges increasing the likelihood. Works only, if trans.close=FALSE. Default: FALSE
mode
For Bayesian network inference and GNEMs: binary_ML: effects come from a binomial distribution - ML learning of parameters (Bayesian networks only); binary_Bayesian: effects come from a binomial distribution - Bayesian learning of parameters (Bayesian networks only); continous_ML: effects come from a normal distribution - ML learning of parameters; continous_Bayesian: effects come from a normal distribution - Bayesian learning of parameters.
nu.intervention, lambda.intervention
For gnem: For any perturbed node we suppose the unknown mean mu given its unknown variance sigma2 to be drawn from N(nu.intervention, sigma2/lambda.intervention). Default: nu.intervention=0.6, lambda.intervention=4
nu.no_intervention, lambda.no_intervention
The same parameters for unperturbed nodes. Default: nu.no_intervention=0.95, lambda.no_intervention=4
df.intervention, scale.intervention
For gnem: The unknown variance sigma2 for perturbed nodes is supposed to be drawn from Inv-$chi^2$(df.intervention, scale.intervention). Default: df.intervention=4.4, scale.intervention=4.4
df.no_intervention, scale.no_intervention
The same parameters for unperturbed nodes. Default: df.no_intervention=4.4, scale.no_intervention=0.023
map
For gnem: Mapping of interventions to network nodes. The format is a named list of strings with names being the interventions and entries being the network nodes. Default: Entries and names are the network nodes.
outputdir
Directory where to put diagnostic plots. Default: folder "QualityControl" in current working directory
debug
Print out or plot diagnostic information. Default: FALSE

Value

A list containing all parameters described above.

Author(s)

Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>

Examples

control = set.default.parameters(LETTERS[1:5], type="CONTmLLBayes", selEGenes=TRUE) # set inference type and whether to use automatic E-gene selection for a network with nodes "A"-"E".

[Package nem version 2.8.0 Index]