previousTrained {ClassifyR} | R Documentation

## Automated Usage of Previously Created Classifiers

### Description

Uses the trained classifier of the same cross-validation iteration of a previous classification for the current classification task.

### Usage

      ## S4 method for signature 'ClassifyResult'
    previousTrained(classifyResult, .iteration, verbose = 3)

### Arguments

`classifyResult` | A `ClassifyResult` object which stores the models fitted previously.
`.iteration` | Do not specify this variable. It is set by `runTests` if this function is being repeatedly called by `runTests`.
`verbose` | Default: 3. A number between 0 and 3 for the amount of progress messages to give. This function only prints progress messages if the value is 3.

### Value

A trained classifier from a previously completed classification task.

### Author(s)

Dario Strbenac

### Examples

      #if(require(sparsediscrim))
      #{
        # 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))))
        colnames(genesMatrix) <- paste("Sample", 1:50)
        rownames(genesMatrix) <- paste("Gene", 1:100)
        classes <- factor(rep(c("Poor", "Good"), each = 25))
        resubstitute <- ResubstituteParams(nFeatures = seq(10, 100, 10),
                            performanceType = "error", better = "lower")
        result <- runTests(genesMatrix, classes, datasetName = "Example",
                           classificationName = "Differential Expression",
                           permutations = 2, fold = 2,
                           params = list(SelectParams(), TrainParams(), PredictParams()))
        models(result)

        # Genes 50 to 74 have differential expression in new data set.
        newDataset <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
        newDataset <- cbind(newDataset, rbind(sapply(1:25, function(sample) rnorm(49, 9, 2)),
                                              sapply(1:25, function(sample) rnorm(25, 14, 2)),
                                              sapply(1:25, function(sample) rnorm(26, 9, 2))))

        rownames(newDataset) <- rownames(genesMatrix)
        colnames(newDataset) <- colnames(genesMatrix)

        newerResult <- runTests(newDataset, classes, datasetName = "Latest Data Set",
                                classificationName = "Differential Expression",
                                permutations = 2, fold = 2,
                                params = list(SelectParams(previousSelection,
                                                           intermediate = ".iteration",
                                                           classifyResult = result),
                                              TrainParams(previousTrained,
                                                          intermediate = ".iteration",
                                                          classifyResult = result),
                                              PredictParams()))

        models(newerResult)
      #}

[Package ClassifyR version 2.12.0 Index]