nem {nem} | R Documentation

## Nested Effects Models - main function

### Description

The main function to perform model learning from data

### Usage

    nem(D,inference="nem.greedy",models=NULL,control=set.default.parameters(setdiff(unique(colnames(D)),"time")), verbose=TRUE)

    ## S3 method for class 'nem':
    print(x, ...)

### Arguments

`D` | data matrix with experiments in the columns (binary or continious)
`inference` | `search` to use exhaustive enumeration, `triples` for triple-based inference, `pairwise` for the pairwise heuristic, `ModuleNetwork` for the module based inference, `nem.greedy` for greedy hillclimbing, `nem.greedyMAP` for alternating MAP optimization using log odds or log p-value densities
`models` | a list of adjacency matrices for model search. If NULL, an exhaustive enumeration of all possible models is performed.
`control` | list of parameters: see `set.default.parameters`
`verbose` | do you want to see progression statements? Default: TRUE
`x` | nem object
`...` | other arguments to pass

### Details

If parameter Pm != NULL and parameter lambda == 0, a Bayesian approach to include prior knowledge is used. Alternatively, the regularization parameter lambda can be tuned in a model selection step via the function `nemModelSelection` using the BIC criterion. If automated subset selection of effect reporters is used and parameter type == CONTmLLMAP, the regularization parameter delta is tuned via the AIC model selection criterion. Otherwise, an iterative algorithm is executed, which in an alternating optimization scheme reconstructs a network given the current set of effect reporters and then selects the effect reporters having the highest likelihood under the given network. The procedure is run until convergence.

The function `plot.nem` plots the inferred phenotypic hierarchy as a directed graph, the likelihood distribution of the models (only for exhaustive search) or the posterior position of the effected genes.

### Value

`graph` | the inferred directed graph (graphNEL object)
`mLL` | log posterior marginal likelihood of final model
`pos` | posterior over effect positions
`mappos` | MAP estimate of effect positions
`selected` | selected E-gene subset
`LLperGene` | likelihood per selected E-gene
`control` | hyperparameter as in function call

### Author(s)

Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>, Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

### See Also

`set.default.parameters`, `nemModelSelection`, `nem.jackknife`, `nem.bootstrap`, `nem.consensus`, `local.model.prior`, `plot.nem`

### Examples

       data("BoutrosRNAi2002")
       D <- BoutrosRNAiDiscrete[,9:16]
       control = set.default.parameters(unique(colnames(D)), para=c(0.13, 0.05))
       res1 <- nem(D,inference="search", control=control)
       res2 <- nem(D,inference="pairwise", control=control)
       res3 <- nem(D,inference="triples", control=control)
       res4 <- nem(D,inference="ModuleNetwork", control=control)
       res5 <- nem(D,inference="nem.greedy", control=control)
       res6 = nem(BoutrosRNAiLods, inference="nem.greedyMAP", control=control)

       par(mfrow=c(2,3))
       plot.nem(res1,main="exhaustive search")
       plot.nem(res2,main="pairs")
       plot.nem(res3,main="triples")
       plot.nem(res4,main="module network")
       plot.nem(res5,main="greedy hillclimber")
       plot.nem(res6,main="alternating MAP optimization")

[Package nem version 2.8.0 Index]