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]