set.default.parameters {nem} | R Documentation |
Allows to set and retrieve various hyperparameters for different inference methods.
set.default.parameters(Sgenes, ...)
Sgenes |
character vector of S-gene identifiers |
... |
parameters to set (see 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.
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).mLL
FULLmLL()
for binary datagnem
. Default: NULLlocal.model.prior
according to arguments local.prior.size
and local.prior.bias
nemModelSelection
is used, Default: 0 (no regularization)nem.greedyMAP
and gnem
. Default: TRUEbinary_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.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=4gnem
: 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=4.4gnem
: 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.
A list containing all parameters described above.
Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>
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".