elasticNetSolver {trena} | R Documentation

## Run the Elastic Net Solvers

### Description

Given a TReNA object with either LASSO or Ridge Regression as the solver, use the `glmnet` function to estimate coefficients for each transcription factor as a predictor of the target gene's expression level.

### Usage

    elasticNetSolver(
      obj,
      target.gene,
      tfs,
      tf.weights,
      alpha,
      lambda,
      keep.metrics
    )

### Arguments

`obj` | An object of class Solver
`target.gene` | A designated target gene that should be part of the mtx.assay data
`tfs` | The designated set of transcription factors that could be associated with the target gene.
`tf.weights` | A set of weights on the transcription factors (default = rep(1, length(tfs)))
`alpha` | The LASSO/Ridge tuning parameter
`lambda` | The penalty tuning parameter for elastic net
`keep.metrics` | A binary variable indicating whether or not to keep metrics

### Value

A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters

### See Also

`glmnet`

[Package trena version 1.16.0 Index]