elasticNetFeatures {ClassifyR} | R Documentation |
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.
## S4 method for signature 'multnet' elasticNetFeatures(model)
model |
A fitted multinomial GLM which was created by |
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.
Dario Strbenac
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]]) }