RidgeSolver {trena} | R Documentation

## Create a Solver class object using the Ridge solver

### Description

Create a Solver class object using the Ridge solver

### Usage

    RidgeSolver(
      mtx.assay = matrix(),
      targetGene,
      candidateRegulators,
      regulatorWeights = rep(1, length(candidateRegulators)),
      alpha = 0,
      lambda = numeric(0),
      keep.metrics = FALSE,
      quiet = TRUE
    )

### Arguments

`mtx.assay` | An assay matrix of gene expression data
`targetGene` | A designated target gene that should be part of the mtx.assay data
`candidateRegulators` | The designated set of transcription factors that could be associated with the target gene
`regulatorWeights` | A set of weights on the transcription factors (default = rep(1, length(tfs)))
`alpha` | A parameter from 0-1 that determines the proportion of LASSO to ridge used in the elastic net solver, with 0 being fully ridge and 1 being fully LASSO (default = 0.9)
`lambda` | A tuning parameter that determines the severity of the penalty function imposed on the elastic net regression. If unspecified, lambda will be determined via permutation testing (default = numeric(0)).
`keep.metrics` | A logical denoting whether or not to keep the initial supplied metrics versus determining new ones
`quiet` | A logical denoting whether or not the solver should print output

### Value

A Solver class object with Ridge as the solver

### See Also

`solve.Ridge`, `getAssayData`

Other Solver class objects: `BayesSpikeSolver`, `BicorSolver`, `EnsembleSolver`, `HumanDHSFilter-class`, `LassoPVSolver`, `LassoSolver`, `PearsonSolver`, `RandomForestSolver`, `Solver-class`, `SpearmanSolver`, `XGBoostSolver`

### Examples

    ## Not run:
       load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
       target.gene <- "MEF2C"
       tfs <- setdiff(rownames(mtx.sub), target.gene)
       ridge.solver <- RidgeSolver(mtx.sub, target.gene, tfs)

    ## End(Not run)

[Package trena version 1.16.0 Index]