run,RandomForestSolver-method {trena} | R Documentation

## Run the Random Forest Solver

### Description

Given a TReNA object with RandomForest as the solver, use the `randomForest` function to estimate coefficients for each transcription factor as a predictor of the target gene's expression level.

### Usage

    ## S4 method for signature 'RandomForestSolver'
    run(obj)

### Arguments

`obj` | An object of class TReNA with "randomForest" as the solver string

### Value

A data frame containing the IncNodePurity for each candidate regulator. This coefficient estimates the relationship between the candidates and the target gene.

### See Also

`randomForest`, `RandomForestSolver`

Other solver methods: `run,BayesSpikeSolver-method`, `run,BicorSolver-method`, `run,EnsembleSolver-method`, `run,LassoPVSolver-method`, `run,LassoSolver-method`, `run,PearsonSolver-method`, `run,RidgeSolver-method`, `run,SpearmanSolver-method`, `run,XGBoostSolver-method`

### Examples

    # Load included Alzheimer's data, create a TReNA object with Random Forest as solver, and solve
    load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
    targetGene <- "MEF2C"
    candidateRegulators <- setdiff(rownames(mtx.sub), targetGene)
    rf.solver <- RandomForestSolver(mtx.sub, targetGene, candidateRegulators)
    tbl <- run(rf.solver)

[Package trena version 1.16.0 Index]