R/Weka Classifier Functions
R interfaces to Weka regression and classification function learners.
LinearRegression(formula, data, subset, na.action, control = NULL) Logistic(formula, data, subset, na.action, control = NULL)
- a symbolic description of the model to be fit.
- an optional data frame containing the variables in the model.
- an optional vector specifying a subset of observations to be used in the fitting process.
- a function which indicates what should happen when
the data contain
- a character vector with control options, or
NULL(default). Available options can be obtained on-line using the Weka Option Wizard
WOW, or the Weka documentation.
There is a
predict method for
predicting from the fitted models.
LinearRegression builds suitable linear regression models,
using the Akaike criterion for model selection.
Logistic builds multinomial logistic regression models based on
ridge estimation (le Cessie and van Houwelingen, 1992).
The model formulae should only use + to indicate the variables to be included.
- A list inheriting from classes
Weka_classifierswith components including
classifier a reference (of class
jobjRef) to a Java object obtained by applying the Weka
buildClassifiermethod to build the specified model using the given control options.
predictions a numeric vector or factor with the model predictions for the training instances (the results of calling the Weka
classifyInstancemethod for the built classifier and each instance).
call the matched call.