R: An Interface for mnlogit Package's mnlogit Function

logisticRegressionInterface {ClassifyR}

R Documentation

An Interface for mnlogit Package's mnlogit Function

Description

logisticRegressionTrainInterface generates a multinomial logistic regression model trained on some training data and logisticRegressionPredictInterface makes class predictions for samples in the test data set.

Usage

  ## S4 method for signature 'matrix'
logisticRegressionTrainInterface(measurements, classes, ...)
  ## S4 method for signature 'DataFrame'
logisticRegressionTrainInterface(measurements, classes, ..., verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
logisticRegressionTrainInterface(measurements, targets = names(measurements), ...)
  ## S4 method for signature 'mnlogit,matrix'
logisticRegressionPredictInterface(model, test, ...)
  ## S4 method for signature 'mnlogit,DataFrame'
logisticRegressionPredictInterface(model, test, classes = NULL, verbose = 3)
  ## S4 method for signature 'mnlogit,MultiAssayExperiment'
logisticRegressionPredictInterface(model, test, targets = names(test), ...)

Arguments

`measurements`	Either a `matrix`, `DataFrame` or `MultiAssayExperiment` containing the training data. For a `matrix`, the rows are features, and the columns are samples.
`classes`	Either a vector of class labels of class `factor` of the same length as the number of samples in `measurements` or if the measurements are of class `DataFrame` a character vector of length 1 containing the column name in `measurement` is also permitted. Not used if `measurements` is a `MultiAssayExperiment` object.
`test`	An object of the same class as `measurements` with no samples in common with `measurements` and the same number of features as it. Also, if a `DataFrame`, the `class` column must be absent.
`targets`	If `measurements` is a `MultiAssayExperiment`, the names of the data tables to be used. `"clinical"` is also a valid value and specifies that integer variables from the clinical data table will be used.
`model`	A fitted model as returned by `logisticRegressionTrainInterface`.
`...`	Variables not used by the `matrix` nor the `MultiAssayExperiment` method which are passed into and used by the `DataFrame` method or optional settings that are passed to `mnlogit`.
`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.

Details

If measurements is an object of class MultiAssayExperiment, the factor of sample classes must be stored in the DataFrame accessible by the colData function with column name "class".

This wrapper works with individual-specific variables. If more a complex experimental design is utilised, such as a market research data set with both individual-specific and alternative-specific variables, then this wrapper is not suitable to classify it.

Value

For logisticRegressionTrainInterface, a fitted multinomial logistic regression model. For logisticRegressionPredictInterface, a factor vector with class predictions for the samples in the test set.

Author(s)

Dario Strbenac

Examples

  if(require(mnlogit))
  {
    variables <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
    trainSamples <- c(1:45, 51:95, 101:145)
    testSamples <- c(46:50, 96:100, 146:150)
    
    trained <- logisticRegressionTrainInterface(DataFrame(iris[trainSamples, variables]),
                                                iris[trainSamples, "Species"])
    predicted <- logisticRegressionPredictInterface(trained,
                                         DataFrame(iris[testSamples, variables]))
  }

[Package ClassifyR version 2.0.10 Index]