rescalePredictorWeights,Solver-method {trena} | R Documentation

## Rescale the Predictor Weights

### Description

Solvers such as LASSO penalize predictors on a scale of 1 (full weight) to infinity (zero weight). With the `rescalePredictorWeights` method, incoming raw values can be scaled between a possibly theoretical minimum and maximum value.

### Usage

    ## S4 method for signature 'Solver'
    rescalePredictorWeights(obj, rawValue.min, rawValue.max, rawValues)

### Arguments

`obj` | An object of the Solver class
`rawValue.min` | The minimum value of the raw expression values
`rawValue.max` | The maximum value of the raw expression values
`rawValues` | A matrix of raw expression values

### Value

A matrix of the raw values re-scaled using the minimum and maximum values

### Examples

    # Create a LassoSolver object using the included Alzheimer's data and rescale the predictors
    load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
    targetGene <- "MEF2C"
    candidateRegulators <- setdiff(rownames(mtx.sub), targetGene)
    ls <- LassoSolver(mtx.sub, targetGene, candidateRegulators)
    raw.values <- c(241, 4739, 9854, 22215, 658334)
    cooked.values <- rescalePredictorWeights(ls, rawValue.min = 1, rawValue.max = 1000000, raw.values)

[Package trena version 1.16.0 Index]