elasticNetFeatures {ClassifyR} | R Documentation

## Extract Vectors of Ranked and Selected Features From an Elastic Net GLM Object

### Description

Provides a ranking of features based on the magnitude of fitted GLM coefficients. Also provides the selected features which are those with a non-zero coefficient.

### Usage

      ## S4 method for signature 'multnet'
    elasticNetFeatures(model)

### Arguments

`model` | A fitted multinomial GLM which was created by `glmnet`.

### Value

An `list` object. The first element is a vector or data frame of ranked features, the second is a vector or data frame of selected features.

### Author(s)

Dario Strbenac

### Examples

        if(require(glmnet))
        {
          # Genes 76 to 100 have differential expression.
          genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
          genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample)
                                            c(rnorm(75, 9, 2), rnorm(25, 14, 2))))
          classes <- factor(rep(c("Poor", "Good"), each = 25))
          colnames(genesMatrix) <- paste("Sample", 1:ncol(genesMatrix))
          rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix))

          resubstituteParams <- ResubstituteParams(nFeatures = seq(10, 100, 10),
                                                   performanceType = "balanced error",
                                                   better = "lower")

          # alpha is a user-specified tuning parameter.
          # lambda is automatically tuned, based on glmnet defaults, if not user-specified.
          trainParams <- TrainParams(elasticNetGLMtrainInterface, nlambda = 500)
          predictParams <- PredictParams(elasticNetGLMpredictInterface)
          classified <- runTests(genesMatrix, classes, datasetName = "Example",
                                 classificationName = "Differential Expression",
                                 validation = "fold",
                                 params = list(trainParams, predictParams))

          elasticNetFeatures(models(classified)[[1]])
        }

[Package ClassifyR version 2.2.6 Index]